Conference Agenda (All times are shown in Eastern Daylight Time)

Web-accessible biodiversity databases accept and openly share species observations from the public, benefitting research, conservation, and education. However, public data sharing can also bring harm, for example by facilitating poaching. Databases may mitigate harm by designating certain species as “sensitive” and restricting access to those species data. In this paper, we describe how 39 databases that share participatory science data justify automatic data access restrictions based on species-related concerns. We developed a codebook describing rationales for restricting access to sensitive species data and analyzed rationale use in relation to database characteristics such as size, type, host institution type, national origin, and taxonomic scope. We found a small set of commonly used rationales, wide variation in the number of rationales provided, and a surprising number of databases citing few rationales. Larger databases, aggregators, and governmental databases tended to cite more rationales, but there were numerous outliers. We hope our framework of rationales will support databases seeking to document data restrictions and assist with the creation of controlled vocabulary terms. The study also provides examples that could guide data curation education about responsible policy development.

Research data archives use restricted data access protocols to manage access to sensitive data. However, restricted data access systems can be cumbersome for researchers to engage in data reuse, as the systems frequently implemented introduce friction into the research process. We fielded a survey of 481 data reusers at the Inter-university Consortium for Political and Social Research (ICPSR) in 2020 about restricted data access systems. We found that 80% of respondents would be more likely to reuse data if restricted data access applications were made faster and easier. Additionally, most researchers indicated they believe that the security of research data is very important. However, researchers disagreed on the appropriate set of mechanisms to ensure that research data remains secure, especially discounting interventions that introduce friction to accessing data. These findings present challenges for archives in implementing restricted data access systems that balance protecting research subjects with encouraging data reuse.

Generative search engines address limitations of traditional data retrieval systems, including rigid keyword-based queries, impersonalized results, and choice overload. However, they introduce new challenges such as prompt literacy demands, hallucination risks, and reduced output diversity. While these trade-offs fundamentally reshape user interactions with search systems, the comparative dynamics of search behavior across generative and traditional systems remain underexplored. This study bridges this gap by analyzing data search behaviors through a search stage framework, revealing distinct interaction patterns. Building upon the Information Search Process Model and Information Seeking Behavior Model, this study proposes a stage model of data search behavior. Experimental data were analyzed to explore the proposed model. Our findings identify both convergent and divergent behavioral patterns: while certain search stage types and behaviors overlap across systems, substantial differences emerge in stage transition dynamics (encompassing transition types, frequencies, and pathways) and specific behaviors. This study uncovers a fundamental tension in data search: traditional retrieval systems support broad exploratory patterns but constrain interaction depth, while generative search engines enable deeper engagement at the expense of exploration breadth. This trade-off between breadth and depth presents significant implications for the design of next-generation intelligent retrieval systems that optimize both dimensions of user interaction.

Interactive visualization of large scholarly knowledge graphs combined with LLM reasoning shows promise but remains under-explored. We address this gap by developing an interactive visualization system for the Cell Map for AI Talent Knowledge Graph (28,000 experts and 1,179 biomedical datasets). Our approach integrates WebGL visualization with LLM agents to overcome limitations of traditional tools such as Gephi, particularly for large-scale interactive node handling. Key functionalities include responsive exploration, filtering, and AI-driven recommendations with justifications. This integration can potentially enable users to effectively identify potential collaborators and relevant dataset users within biomedical and AI research communities. The system contributes a novel framework that enhances knowledge graph exploration through intuitive visualization and transparent, LLM-guided recommendations. This adaptable solution extends beyond the CM4AI community to other large knowledge graphs, improving information representation and decision-making. Demo: https://cm4aikg.vercel.app/

Generative artificial intelligence (AI) has experienced rapid advancement, fundamentally transforming the information landscape. This technological shift has not only amplified the dissemination of misinformation but has also posed significant challenges to conventional frameworks of trust and verification. This paper explores the dual impact of AI: its potential to enhance information services while simultaneously amplifying misinformation and disinformation. Seven AI-generated misinformation cases between 2022 and 2025—ranging from deepfakes and political propaganda to impersonation and amplification were analyzed. Through thematic case analysis and interdisciplinary synthesis, the study proposes the AI-Misinformation Resilience Model (AIM-RM), a conceptual framework guiding proactive responses across verification infrastructure, digital literacy, and ethical policy engagement. Drawing on recent scholarly literature and grounded in information ethics, epistemic trust, and sociocultural literacy, the model offers a path forward for LIS professionals seeking to navigate the post-truth era.

This short paper presents a study that analyzes the role of information professionals—specifically digital records management (DRM) and digital preservation (DP)—in the current human-centered artificial intelligence (HCAI) landscape, using the newly enacted EU Artificial Intelligence Act (AIA) as a case study. It argues that these information professionals should be explicitly integrated into the HCAI framework to help achieve its goals and ensure the successful implementation of the AIA.

This interdisciplinary perspective paper explores the evolving relationship between generative artificial intelligence (GenAI) and library reference services across academic and public libraries, with implications for Library and Information Science (LIS) education. As numerous AI tools, especially GenAI, make information easier to access but quality information harder to identify, information professionals have a unique opportunity to lead in the responsible use of AI for information access. In particular, as libraries integrate GenAI into virtual and traditional reference models, they must navigate opportunities for enhanced service delivery and challenges such as AI hallucinations and ethical use. The paper traces historical developments in personalized reference, staffing trends, and technological transformations, arguing that GenAI tools should complement rather than replace human librarians. Additionally, the paper examines the impact of GenAI on archival reference, specialized services, and the emergence of conversational assistants as digital intermediaries. Ethical considerations are also addressed, including misinformation, epistemic agency, and belief formation. These considerations lead to the need for LIS curriculum to evolve to incorporate AI competencies, emphasizing responsible use, evaluation of AI outputs, and the development of AI literacy. The paper concludes by advocating for a human-centered AI approach that reinforces librarians’ roles as ethical guides and information stewards.

Our research explores whether Large Language Models (LLMs) can offer a solution for improving the efficiency of developing detailed, rich metadata for large digitized collections. We tested the ability of seven widely available LLMs to complete four metadata generation tasks for a selection of pages from the Southern Architect and Building News (1882-1932): assigning subject headings; creating short content summaries; extracting named entities; and writing transcriptions. Our cross-departmental team evaluated the quality of the outputs, the cost, and the time efficiency of using LLMs for metadata workflows. To do so, we developed a metadata quality rubric and scoring schematic to ground our results. Analysis suggests that models can perform interpretive metadata tasks well, but lack the accuracy needed for assigning terms from controlled vocabularies. With careful implementation, thorough testing, and creative design of workflows, these models can be applied with precision to significantly enhance metadata for digitized collections.

Embodied cultural heritage information resources carry the bodily experiences and perceptual logic formed through human cultural practices, exhibiting high multimodality, immersiveness, and dynamic transmission characteristics. However, traditional methods of information organization struggle to reveal the tacit embodied knowledge within them. To address this, this study focuses on typical embodied cultural heritage information resources represented by the guqin jianzi notations, constructing an embodied semantic ontology model and proposing a bidirectional collaborative mechanism integrating large language model with ontology model to enhance the semantic understanding of embodied knowledge by large language models. The study constructed two embodied knowledge base, puzi knowledge base and jianzi knowledge base, and conducted multiple experiments to evaluate the performance of large language model in understanding the semantics of single jianzi and jianzi sequences. The results show that the embodied knowledge bases significantly enhance large language model’s ability to interpret complex cultural symbols, particularly improving accuracy in understanding embodied semantic dimensions such as fingering techniques, hui positions, and timbre. Theoretically, this study expands new pathways for semantic modeling of embodied cultural heritage; practically, it provides a feasible technical solution for the digital preservation and intelligent transmission of embodied cultural heritage.

This paper explores how users perceive and interact with AI in a crowdsourcing ancient texts digitization project. We conducted semi-structured interviews with 28 participants from the ancient text digitization crowdsourcing project “I am a Collator of Ancient Texts”. Participants viewed AI recognition as accurate and efficient but noted its limits with deteriorated text quality and complex layouts. We found that AI misidentification or failure to provide alternative character suggestions may reduce users’ task completion efficiency or even influence their task selection. A key theme emerging from the study is user trust development when interacting with AI—despite initial skepticism, trust in AI technologies gradually increased with positive interaction experiences, leading users to adjust their validation strategies. We also observed that participants acquired knowledge from AI recognition and suggestion results, but could also be misled when AI made errors. This study contributes to understanding human-AI collaboration in crowdsourced cultural heritage digitization and suggests that future platforms should provide customizable confidence indicators, clearer AI explanations, and learning supports to accommodate diverse users.

In recent years, cultural heritage institutions have experimented with opening up cultural heritage data and encouraging its reuse. While a growing body of digital humanities research has focused on the value of open data for cultural heritage and the development of data infrastructures, there is limited work exploring the searching behavior for open dataset in the cultural heritage domain at the micro level. This paper examines cultural heritage data curation competitions as a case to explore the open data searching behaviors of participants in such information practices. Using semi-structured interviews, field observations, and secondary data analysis, this study identifies participants' data needs, search strategies, evaluation criteria, and related challenges through open coding. The findings will help cultural heritage institutions to effectively organize and curate open datasets, and advocate for digital libraries to better integrate open data resources and build open data platforms.

Making visible LGBTQIA+ materials within cultural heritage institutions requires understanding the potentially positive and negative consequences of queer visibility. Deploying findings from interviews with 29 archivists, this paper explores how practitioners responsible for creating and managing finding aids for LGBTQIA+ archives navigate their work. This paper reveals how participants navigated clear understandings of the historical and theoretical implications rooted in naming queer identities within archival records. This paper places these configurations of archival description and queer visibility in conversation with both the reparative and anti-social lenses of queer theory. The first posits that expanding queerness in finding aids ensures a more queer-inclusive future, while the latter contends that inconsistencies between social support of queer individuals and the policies enacting such support remain too vast to ensure safety. The paper concludes with theoretical and praxis-based implications for this work in an era of automation and governmental backlash.

This paper explores the phenomenon of news deserts as information problems to navigate research opportunities and theorize its dynamics. Drawing on the theory of local information landscapes, news deserts are conceptualized as more than merely an absence of news organizations or content; rather, emphasizing the structural and material dimensions of local news ecosystems, such as fragmentation, transience, and inconsistent distribution. We argue that news deserts should be understood as material pre-conditions of people’s inability to access, interpret, and engage with local information. To empirically ground this conceptualization, we conduct a case study in Alabama, using over 30,000 news articles from a local news operation. Through geographic, thematic, and temporal analyses, paired with population and crime statistics, we uncover patterns of underreporting, geographic bias, and thematic concentration. Our findings demonstrate that news deserts can emerge even in areas served by active news outlets, contributing to a broader understanding of the uneven distribution of local information, vital for civic engagement and community well-being.

AI and data are mutually influential, with AI outputs shaped by training data and data often generated, processed, and categorized by AI. The use of both AI and data by government organizations is guided by policy documents; existing research has explored data policies or AI policies but has rarely put both in conversation, despite their linked subject matter. We adopt a mixed-methods approach to analyze the data and AI strategies of the Government of Canada, investigating whether the data-AI relationship is reflected in policy documents. Our findings demonstrate a disconnect between Canadian data and AI policies, illustrate potential implications of this disconnect, and contribute to ASIS&T 2025 conversations about the necessity of information science for the responsible, ethical use of data and AI in government settings.

Increasing political polarization poses a significant threat to democratic functioning, hindering constructive dialogue and collaborative problem-solving. This paper explores the potential of information science, specifically through the application of sensemaking tools employing Artificial Intelligence (AI), to address this challenge. Drawing on theories of sensemaking and framing, we examine how technology can support structured dialogue initiatives by using sensemaking tools to support the political depolarization work of the non-profit organization Braver Angels. We introduce two specific sensemaking tools – Issue Sensemaking and Article Sensemaking – designed to help individuals explore complex political issues from multiple perspectives, analyze information sources critically, and identify areas of common ground and disagreement. By facilitating deeper understanding and more structured engagement with diverse viewpoints and information artifacts, these tools offer a promising avenue for improving the quality of civic discourse and potentially reducing affective polarization. This work aligns with the need for information science to contribute responsible, human-centered solutions in turbulent socio-political contexts.

The study examines the datafication process in China's elderly care service policies through the lenses of the data value chain and data gaze frameworks. As China faces rapid population aging, digital transformation has become a strategic priority to enhance care quality and efficiency. The research analyzes 103 national policies (2011–2024) to map how data value is created across four stages: collection, organization, circulation, and utilization. Findings reveal an uneven policy focus, with heavy emphasis on foundational infrastructure (data collection and organization) but limited attention to data utilization and ethical governance. The data gaze analysis highlights a top-down vision prioritizing technical systems and regulatory control, often overlooking user inclusivity and practical implementation challenges. While policies have established platforms for data integration and sharing, gaps persist in capacity building, stakeholder collaboration, and equitable service delivery. The study contributes theoretically by integrating the data value chain and data gaze to critique policy frameworks, and practically by identifying actionable areas for improvement, such as fostering inclusive participation and strengthening late-stage data value creation. The study offers valuable lessons for global research on public sector data governance.

The integration of artificial intelligence (AI) into the intimate sphere of human relationships presents a profound challenge to traditional understanding of intimacy, necessitating a critical examination of this technological development into a domain that has always been viewed as deeply humane. Built on the Computers Are Social Actors (CASA) paradigm, this study utilizes in-depth semi-structured interviews (N = 23) to understand human-AI intimate relationships in the Chinese culture at the intersection of traditions and modernization. A Context-Script-Cue (CSC) model of three mechanisms emerge in how users engage in human-AI intimate relationships – social-cue transformation, interpersonal-script application, and interaction-context reconstruction – through which users navigate emotional artificial intimacy woven by AI. By empirically examining how users engage in human-AI intimate relationships, this study sheds light on key ongoing debates regarding the CASA paradigm and offers a new CSC framework for researching the evolution of human-AI intimacy in different cultures.

Poetry therapy is a promising non-drug approach to mental health. It offers unique benefits through short but emotionally powerful literary works. With its rich cultural heritage and aesthetic elements, classical Chinese poetry holds significant therapeutic potential yet remains underutilized due to accessibility barriers and limited professional resources. This paper presents a Generative AI (GenAI)-driven poetry therapy system. It connects classical Chinese poetry with modern therapeutic practices through three stages: poetry recommendation, guided creation, and recitation visualization. Our system leverages Retrieval-Augmented Generation to ensure cultural accuracy while providing personalized therapeutic experiences through MLLM’s multimedia generation capacity. Preliminary evaluation with 60 Chinese participants demonstrates positive reception across emotional engagement (M = 4.18/5) and cultural experience dimensions (M = 4.30/5), suggesting the system’s effectiveness in both mental well-being support and cultural transmission. Our framework provides empirical insights for developing human-AI collaboration poetry therapy systems that preserve cultural heritage while enhancing accessibility and therapeutic engagement.

Individuals with schizophrenia frequently experience intense emotions and often turn to vlogging as a medium for emotional expression. While previous research has predominantly focused on text-based disclosure, little is known about how individuals construct narratives around emotions and emotional experiences in video blogs. Our study addresses this gap by analyzing 200 YouTube videos created by individuals with schizophrenia. Drawing on media research and self-presentation theories, we developed a visual analysis framework to disentangle these videos. Our analysis revealed diverse practices of emotion disclosure through both verbal and visual channels, highlighting the dynamic interplay between these modes of expression. We found that the deliberate construction of visual elements—including environmental settings and specific aesthetic choices—appears to foster more supportive and engaged viewer responses. These findings underscore the need for future large-scale quantitative research examining how visual features shape video-mediated communication on social media platforms. Such investigations would inform the development of care-centered video-sharing platforms that better support individuals managing illness experiences.

Many college students experience mental health challenges and actively utilize social media platforms, such as YouTube, for informational and social support. Despite its popularity, there is limited understanding of the specific mental health-related topics that young adults discuss on YouTube and how users engage with them. This study employed topic modeling, sentiment analysis, and statistical techniques to analyze comments on YouTube videos pertaining to college students’ mental health. Our primary objective was to identify prevalent topics, sentiments, and their associations with user engagement. We found that situational topics (e.g., parents, schools) addressing their mental health challenges were mostly negative and associated with greater engagement from the community than other topics. Our findings provide insights into the perceptions and discussions surrounding college students’ mental health issues within the broader online community and have implications for clinical practice.

Rural Chinese older people face significant information poverty and elevated health anxiety, yet their health information behavior remains understudied in non-Western contexts. This study employs in-depth interviews, grounded theory, and information horizon mapping to investigate the health information seeking patterns of this vulnerable group. Our findings reveal a concentric life circle pattern, where health information behavior radiates outward from the core life circle (kinship ties/close community) to transitional life circles (extended networks). By integrating Chatman's small world theory and life in the round theory with China’s localized context, we propose the original "Concentric Life Circle Theory" (CLCT). This theory advances cross-cultural information behavior research and offers actionable solutions to mitigate health anxiety through culturally tailored information interventions.

Generative Artificial intelligence (GenAI) has provided opportunities for multimodal expressions among people who lack AI literacy like older adults. A 10-lesson AI literacy for lifelong learning (AILL) programme was designed for 23 older adults aged 56 to 75 in Hong Kong to enable them to interact with AI to create songs, images, and stories. Adopting a mixed-methods approach, this study investigated the impact of AILL on the participants’ AI literacy, well-being, social connection and collaboration. Results indicated that the AILL programmes significantly enhanced the participants’ psychological well-being, life satisfaction, and reduced loneliness. Qualitative interviews identified four key themes: AI literacy, well-being, socialization and ethical concerns. These themes highlight technological, psychological, ethical, and pedagogical dimensions, offering valuable insights for policymakers and professionals in gerontology. This study enhances the existing AI literacy model by incorporating the concept of “Enable AI” to advance the framework to GenAI literacy. This updated model aims to foster an AI-inclusive society that supports and empowers older adults who can learn other knowledge with the support of AI.

As artificial intelligence (AI) increasingly shapes how individuals seek and evaluate information, older adults (75+) encounter distinct challenges in navigating AI-driven systems. While AI-powered tools can enhance information access, decision-making, and digital communication, older users may struggle with algorithm opacity, trust calibration, and cognitive overload, leading to misinterpretation, overreliance, or disengagement. This paper introduces a theoretical framework that examines how older adults interact with AI-based information technologies through three interrelated mechanisms: cognitive adaptation, trust calibration, and behavioral reinforcement. By integrating insights from cognitive science, human-computer interaction, and information behavior, the framework highlights the barriers older users face and the strategies needed to improve AI engagement. The study identifies key design considerations, including progressive model refinement, transparent feedback mechanisms, and user-driven customization, to enhance explainability, trust, and usability in AI systems tailored for aging populations. As a first step in a larger empirical study, this research lays the groundwork for future qualitative and quantitative investigations into how older adults navigate AI-generated information, assess reliability, and develop long-term AI engagement patterns. Overall, this study contributes to the broader effort to create inclusive, user-friendly, and transparent AI-driven information environments for older adults.

This study explored whether the application of generative search (GS) techniques—including RAG, COT, and RAT—is superior to traditional keyword search (KS) techniques in digital museum scenarios. We constructed a prototype search system using 800 painting collections from the digital collection repository platform of the Palace Museum in Beijing. Sixteen users performed factual, explanatory, and exploratory search tasks while evaluating five search methods, including KS and various GS optimizations. The experiment results reveal that GS performs better than KS, despite challenges with user satisfaction and search accuracy because of vague responses and occasional hallucinations. Among GS techniques, RAT—integrating chain-of-thought reasoning with document retrieval—achieved the best overall performance. The findings help overcome current search challenges in digital cultural heritage and provide practical guidance for advancing the intelligent development of online museums.

This paper describes an exploratory study applying an AI chatbot to the transformation of archival collections into linked data representations in a community archive setting. Through qualitative analysis of research diaries, we analyze the experiences of novice data curators using ChatGPT to develop item-level metadata records conformant to linked data standards based on a collection of informally digitized items. Findings indicate that users find ChatGPT useful for a few, discrete archival processing tasks with limited types of items, but ChatGPT’s significant inconsistencies impede systematized workflows. Our findings shed light on anticipated benefits and challenges of using generative AI to create linked data from digital collections in resource-limited settings, relevant to archival institutions and community organizations seeking low-barrier opportunities to make their collections more accessible and interconnected online.

Cultural heritage resource metadata is the foundation and precious asset for GLAM institutions to provide knowledge services which enables users to efficiently search relevant collection information. However, current GLAM institutions (e.g. museums), face significant challenges to gather comprehensive high-quality collection metadata. Motivated by the complementary advantages of multi-modal large language models (MLLMs) and pretrained small ones (MPSMs), we proposed “AGGM”, a model fusion approach for automatic metadata enrichment including two key components: MPSM-based module for demonstration detection and MLLM-based module for prediction calibration. The merit of AGGM is to fully leverage the powerful semantic understanding capabilities to generate accurate results based on MLLMs in limited computation cost, and also exert the strength of MPSM domain-specific knowledge to obtain informative demonstrations. The experimental results showed that AGGM outperformed baseline models in two regular metadata generation tasks, demonstrating enormous potential of this proposed model fusion approach in automatic generation of GLAM metadata.

We share results from a mixed-method, three-year grant-funded investigation into whether Library, Archive, and Museum (LAM) organizations can integrate crowdsourced transcription data into content management systems (CMSs) or databases, and whether doing so makes primary sources such as diaries and letters discoverable and accessible for blind people or those with low vision (BLV). Our research questions are concerned with the ease and ability of LAMs to integrate crowdsourced data, LAM and academic crowdsourcing project owners’ beliefs and attitudes about such data, and the discoverability and accessibility of crowdsourced data for BLV users. This paper shares findings from a survey and two interview types conducted with 12 LAM Partner organizations to understand practitioner attitudes about crowdsourced data quality, and data ingest tools, systems, and processes. We applied Evaluation and Magnitude Coding to our LAM Partner data. We hypothesized that crowdsourced data might be over-collected and underutilized due to complex data structures, distrust in volunteer crowdsourcing, and the absence of usable technical infrastructure and metadata standards to support data integration into LAM CMSs. We found significant heterogeneity in data ingest processes but higher rates of successful ingest than expected: 10 out of 12 LAM Partners had made data publicly available.

With the rapid digital transformation of cultural heritage institutions, virtual guides have emerged as an innovative solution to enhance visitor engagement in museums. However, the effectiveness of these digital guides might depend not only on the technology itself but also on how well they match the artifacts. Grounded in the diffusion of innovations theory, this study investigates how the perceived cultural similarity between a digital guide’s appearance and the cultural style of exhibited artifacts influences visitors’ experience in museums. We conducted a single-factor two-level between-subjects laboratory experiment. The results indicate a mediating role for perceived compatibility with the moderation of openness trait. In addition, visitor experience was found to predict purchase intention. These findings offer new insights into how digital innovations can be more effectively diffused in cultural settings by emphasizing the role of perceived compatibility and providing direction in designing culturally consistent virtual guides to improve visitor experience.

This paper considers emerging adulthood (EA) as a theoretical lens for understanding how Brazilian college students manage misinformation on social media. Data were collected through focus groups with 25 undergraduate library and information science majors at Brazil’s Federal University of Minas Gerais in the weeks surrounding Brazil’s 2022 presidential election, a period marked by intensified misinformation online. Data analysis connects five key developmental factors of EA to participants’ everyday information practices and shows that political tension and family conflicts were linked to experiences with misinformation. It also shows that these students are not passive consumers of information and misinformation but active navigators of the digital information landscape, with some playing the role of information evaluation experts for family and others, especially in WhatsApp personal messaging and on social media. The authors conclude that efforts to combat misinformation must consider emerging adults’ developmental, social, and emotional dimensions, alongside technical and educational interventions.

As deepfake videos become common online and harder to detect, understanding how people identify them is increasingly important. Although previous studies have explored how information literacy self-efficacy, critical thinking and media literacy relate to misinformation detection, few have focused specifically on deepfake videos, which are more visually deceptive and cognitively demanding to examine. While some research has examined the role of detection accuracy, less is known about how confidence interacts with individual traits during the detection process. Thus, this study examines how these three factors relate to people’s confidence and accuracy in identifying deepfake videos. Two hundred participants took part in an online study where they watched and evaluated a set of real and deepfake videos, rated their confidence, and completed a series of questionnaires. The results were analysed using PLS-SEM. Our findings show that all three traits influence detection accuracy, although new media literacy showed a negative effect. Moreover, confidence served as a mediator between new media literacy and detection accuracy. These findings suggest that helping people build stronger media-related skills and confidence may support better identification when examining manipulated video content. The study also highlights the need for investigation into the mechanisms behind miscalibrated confidence.

This paper studies community-driven fact-checking –the members of a community fact-checking their own content– on WhatsApp, with the aim of determining its prevalence, who does it, and whether it is effective. The study leverages

two large datasets of WhatsApp group chats, encompassing both public and private group conversations with varying levels of intimacy among members. Adopting a mixed-methods approach, the research combines quantitative analysis of observational data with qualitative measures to shed light on these research questions.

The findings reveal that community-driven corrections are infrequent, and when they do occur, they are typically conveyed through polite requests aimed at alerting individuals about the presence of misinformation. However, users often exhibit apathy towards self-correction, disregard the corrections, or even feel offended by public corrections within the group. Notably, the responsibility of correcting misinformation primarily falls on active community members, with group administrators accounting for a relatively small portion (up to 20%) of the corrections. Additionally, the study uncovers significant variations in the types of corrections and responses to corrections, influenced by group norms and the degree of familiarity among group members. These observations suggest the existence of underlying dynamics of power and trust within these groups.

Deepfake video detection among Generation Zs remains an understudied area despite growing concerns about their exposure to synthetic content. Previous research has typically focused on adults, emphasising important cues in detection accuracy, but little is known about what strategies Generation Zs utilise. To address this gap, we conducted a study on participants under 21 years old. Participants were shown four videos, two real and two deepfakes, after which they completed an online survey detailing the strategies they used for identification. Our findings show that visual, audio and knowledge-based cues were often used for detection. Audio cues, especially vocal and language features, were frequently used in the correct detection of real videos compared with deepfakes. In contrast, visual cues like facial cues did not show a significant difference in usage between real and deepfake videos, which were often used in incorrect detection of deepfakes.

This study investigates students’ aversion to AI grading systems compared to human professors, focusing on how dissatisfaction with the current evaluation system and grade outcomes affect fairness perceptions. Drawing from 228 college students in South Korea, the experiment tested three hypotheses: (1) students prefer human graders (i.e., AI aversion), (2) dissatisfaction with the current system mitigates the AI aversion, and (3) this mitigation is contingent on whether students receive high or low grades. Results confirm a general aversion to AI graders. Yet, students who are dissatisfied with the status quo system displayed increased preference for AI graders (reduced AI aversion), especially when receiving low grades. In contrast, those receiving high grades continued to prefer human professors regardless of dissatisfaction. These findings suggest that students’ openness to AI graders is shaped by their discontent with the current systems and their self-interest, influencing fairness perceptions in educational settings.

As AI tools become increasingly integrated into academic settings, it is critical to understand their roles in teamwork in collaborative settings. This study aims to understand how AI Usage is reshaping teamwork by examining college students' engagement with AI in group projects, focusing on usage scenarios, perceived AI roles, and task allocation. We conducted semi-structured interviews with 20 undergraduate and graduate students across diverse disciplines and coded the transcripts according to a team-AI collaboration framework. Findings reveal that: 1) students employed AI in various task types, including creative, information processing, output-oriented, and planning tasks, assigning roles such as ideation partner, expert advisor, and time planner depending on the context; 2) while AI use enhanced productivity and efficiency, it also led to reduced human communication, reduced sense of ownership, and concerns about skill development and academic integrity; 3) limited transparency regarding AI use within teams may lead to distrust and uncertainty about individual contributions, highlighting the importance of establishing shared awareness in collaborative work involving AI. The findings suggest that teams should be more deliberate in human-AI task allocation to take advantage of AI capabilities and be more transparent among team members of AI use during collaboration.

Explorations of AI use in higher education have included ethical concerns, though primarily have taken a broad view, focusing on concerns with academic integrity and plagiarism. More nuanced explorations into the ethics of AI use, especially from a qualitative perspective, have been lacking. Utilizing Floridi’s unified framework of ethical principles for AI as a guide, this study addresses this gap with a qualitative exploration of undergraduate students’ perceptions of the ethical underpinnings of AI tools in the context of use for course work completion. Findings suggest that beneficence, non-maleficence, and autonomy are clearly present in students’ perceptions of ethics in AI, but justice and explicability were not. This suggests a deeper understanding of ethics in AI use beyond fear of plagiarism, but a noted lack of understanding around the true nature or impact of AI. These findings invite additional research around students’ understanding of AI and the inclusion of faculty.

On-campus generative AI (GenAI) deployment enhances the digital infrastructure of universities and can provide opportunities for students to interact with GenAI. However, limited research has explored the attitudes of college students initially exposed to on-campus GenAI deployment. This study conducts semi-structured interviews with 13 participants based on the socio-technical configuration perspective. The preliminary findings show that the on-campus GenAI deployment as a socio-technical enacted information practice has four characteristics: AI-focused activities, generative of rules and norms, the inclusion of individual and collective agency, and the embrace of the body and materiality. Furthermore, our findings illuminate students' general understanding of on-campus GenAI deployment, their developing prompt literacy, and their primary concerns and worries regarding its use. This work provides valuable insights into student attitudes toward on-campus GenAI deployment and contributes to a nuanced understanding of such information practices.

As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are becoming increasingly adopted across industries, particularly in healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our study evaluates the privacy practices of 12 widely downloaded AI healthcare chatbot apps available on the App Store and Google Play in the United States. We conducted a three-step assessment analyzing: (1) privacy settings during sign-up, (2) in-app privacy controls, and (3) the content of privacy policies. The analysis identified significant gaps in user data protection. Our findings reveal that half of the examined apps did not present a privacy policy during sign up, and only two provided an option to disable data sharing at that stage. The majority of apps’ privacy policies failed to address data protection measures. Moreover, users had minimal control over their personal data. The study provides key insights for information science researchers, developers and policymakers to improve privacy protections in AI healthcare chatbot apps.

The widespread of AI-generated health misinformation poses significant challenges to public health. This study aims to investigate the influence of eHealth literacy, the ability to seek, comprehend, and appraise health information from digital sources, on individuals’ ability to detect AI-generated (as opposed to human-written) health misinformation and their subsequent sharing behaviors. We conducted an online experiment of 627 participants in which they were presented with 12 messages to detect both AI-generated and human-written misinformation. Results show that: 1) AI-generated information is often perceived as more convincing, regardless of whether the information is true or false. 2) Higher eHealth literacy paradoxically correlates with poorer detection accuracy, especially among younger, healthier participants, exposing a concerning self-assessment gap. 3) Participants with lower accuracy in detecting misinformation are more likely to share health information and less likely to correct it when they are aware of its inaccuracy, thus spreading misinformation further. These findings highlight a disparity and challenge to use self-perceived eHealth literacy as indicators of actual ability of handling health misinformation. Targeted interventions to enhance digital health literacy, particularly among younger and healthier populations, are urgently needed.

People often share health information without adequate verification, which contributes to the growing spread of health misinformation on digital platforms. While previous studies have explored different cognitive and psychological factors underlying such unverified sharing, limited attention has been given to cyberchondria, a pattern of excessive and anxiety-driven online health information seeking. Grounded in the Stressor-Strain-Outcome (SSO) framework, this study proposed a mediated moderation model to link cyberchondria to unverified health information sharing. Utilizing data from a three-wave panel survey conducted in China, the results demonstrate that cyberchondria is positively associated with unverified health information sharing, and this association is partially mediated by information overload. Furthermore, the indirect relationship appears stronger among individuals with higher beliefs in the reliability of their information sources, while it is not statistically significant among those with lower beliefs. These findings highlight the importance of understanding cyberchondria not only as an individual mental health concern but also as a pathological information behavior that contributes to the broader dynamics of misinformation spread in digital health environments.

Generative artificial intelligence (GenAI) has had polarizing effects due to its content-creation capabilities and the systems in which it is integrated; at the same time, GenAI has been undertheorized in information science. By grounding its conceptual inquiry in the literature and examining the scholarly communication ecosystem, this short paper considers the role of GenAI as a “wicked problem” for scholars and publishers in scholarly communication, affecting how information is produced, selected for publication, and consumed. The concept of uncomfortable knowledge is used to explore how GenAI may be accepted or rejected out of hand. Building on this, the human information behavior (HIB) principle of information avoidance further situates the problem by examining how scholars and publishers may resist or ignore information that conflicts with their existing worldviews. By formally intersecting these concepts to analyze GenAI in scholarly communication, this paper addresses an important conceptual lacuna in information science.

This work examines the intricate connections between Generative AI (GenAI), its training data, and the scholarly record through a data-driven discourse analysis. It is common for training datasets for GenAI models to be confidential and proprietary. Consequently, questions about the quality and provenance of the data are often raised. Leaked internal documents suggest that Meta’s Llama3 was trained using pirated data from the file-sharing platform Library Genesis (LibGen) (Reisner, 2015). Given the increasing use and popularity of GenAI in scientific and educational contexts, we investigate metadata from scientific articles in LibGen that were reportedly used to train Llama3 and assess the potential impact of retractions and related concerns identified by Retraction Watch (Retraction Watch Database, 2018) on GenAI output. Using the LibGen API, we identified retracted articles in biomedical science and chemistry, domains known for high retraction rates, and analyzed their retraction reasons. This paper is a preliminary exploration of a complex topic and contributes to discussion surrounding the effects of training data quality on GenAI output, with particular attention to scientific integrity and the ethical implications of data sourcing practices.

Our study introduces a novel bibliometric methodology integrating sigmoid-parameterized lifepaths, regime classification, and topic modeling to analyze the dynamics of scholarly research. Using carbon footprint (CF) research as a case study, our approach moves beyond traditional bibliometric techniques by applying sigmoid modelling to track the research trajectories of authors, countries, and topics. This enables the identification of distinct phases, including acceleration, inflection, saturation, and decline. Our framework incorporates “time folds” to accommodate nonlinear disruptions and catalytic transitions, revealing how external factors – such policies, economic shifts, and global events – shape research progress. Our findings highlight regional disparities in research activity, with China and India exhibiting rapid acceleration, Western nations indicating saturation, and African research remaining fragmented. Topic modelling identifies key research shifts in agriculture, construction, and carbon-free technologies, reflecting evolving global priorities. To facilitate further exploration, we provide an interactive visualization on GitHub, enabling scholars to engage with research lifepaths, analyze thematic shifts, and examine country-level trends. By making these tools and methods openly accessible, this study offers a foundation for researchers to refine and expand the framework across other research fields, ultimately supporting deeper investigations into scholarly ecosystems, emerging trends, and the impact of research policies.

Academic large language models have demonstrated transformative potential in natural language processing tasks. However, they still face significant challenges in adequately understanding highly specialized and complex domain-specific knowledge. To address this issue, this study introduces the Boundary Knowledge Enhance (BKE) framework, which constructs a large-scale, high-quality professional question-answering dataset (n = 276,083) in the Library and Information Science (LIS) domain, specifically designed to capture the complexity of social science knowledge. Furthermore, by employing the proposed Direct Boundary Knowledge Optimization (DBKO) training method, the model’s ability to comprehend and apply specialized domain knowledge is significantly enhanced. Experimental results show that LISGPT achieves superior performance compared to state-of-the-art commercial models. In the literature keyword prediction task, it outperforms all baseline models with an F1 Score of 0.3973, ranking first. In the professional translation task, it reaches 99.1% of the performance level of DeepSeek-V3-671b, achieving an average score of 0.5971 and ranking third. Ablation studies confirm that the overall performance improvement of LISGPT after DBKO training is 2.32%. This study open-sources the large LIS training datasets and three versions of a specialized LIS academic model, offering a practical paradigm for developing open-source, efficient models in other humanities and social sciences domains.

The rapid rise of generative artificial intelligence (AI) is fundamentally reshaping societal operating paradigms. Employing a three-dimensional framework—Policy Instruments, Information Lifecycle, and Governance Actors—grounded in policy instrument theory and the information lifecycle model, 78 Chinese AI policy documents (570 units) were systematically coded. Findings show a balanced distribution across supply-, demand-, and environment-oriented tools, yet notable differences at the subcategory level. Policies focus most on information transmission and use, less on processing, and least on generation and storage. Governance primarily targets providers and facilitators, with limited attention to users. Cross-dimensionally, environmental tools dominate, especially for providers and users, while supply tools are prevalent for facilitators. No significant differences are found in actor participation across lifecycle stages. The analysis offers evidence-based recommendations for optimizing China’s AI governance system.

Understanding public perceptions and government responsiveness through digital platforms is crucial for accountable and ethical policymaking, enhancing the role of e-government users is particularly effective in communicating policy information. This paper applies computational social science methods, including Large Language Model driven content analysis and sentiment analysis, to examine longitudinal trends in citizen appeals related to fertility policy on China’s leading e-petition platform and government responses from social media platform. We identify alignments and mismatches between citizen’s demands and official actions, emphasizing the important role of citizens in digital governance. Findings from our research reveal the fundamental role of government policy information on citizens’ policy literacy and life decisions: Changes in fertility policy influence citizen’s information-seeking behavior, altering the types of information they pursue online. Our study therefore recommends a human-centric approach for policy analytics and highlights inclusivity in digital policy information dissemination.

As a representative of next-generation artificial intelligence, generative AI is profoundly transforming contemporary societal structures. As a pivotal player, China serves as both a primary application market and a key innovator in AI technology, with its developmental trajectory significantly shaped by national policy frameworks. This study employs Latent Dirichlet Allocation (LDA) topic modeling to systematically analyze 78 valid and currently implemented AI policy documents in China. The research aims to identify core focus areas in China's current AI policy landscape and provide insights for sustainable development of AI. Analytical results highlight seven key policy themes: (1) technological innovation and industrial integration, (2) social governance and mechanism evaluation, (3) model training and disciplinary methodologies, (4) software algorithms and data security, (5) pilot zone construction and innovation development, (6) infrastructure and intelligent service systems, and (7) AI research project implementation. Based on these findings, the study concludes with targeted policy recommendations.

Researchers are increasingly seeking to automate systematic review workflows to reduce time and labor. However, AI-generated search strategies often contain errors that can significantly undermine the reliability of the evidence. To evaluate the relevancy and reproducibility of automated search strategies, we conducted a comparative analysis between a human-in-the-loop system with a neurosymbolic AI framework (i.e., NeuroLit Navigator) and three AI systems that primarily rely on generative language models: Scite, Consensus, and Perplexity. Results showed that through an iterative human-in-the-loop approach, NeuroLit Navigator produced more precise and focused search strategies aligned with domain-specific terminologies compared to commercial LLM-based systems, which presented issues such as lack of reproducibility and interpretability. This study highlights the potential of human-AI collaboration in systematic review workflows, suggesting that AI should augment, rather than replace, librarian expertise. Our findings contribute to the growing field of human-centered AI by providing a model for designing AI systems in information-intensive domains.

This study maps the adoption patterns of AI tools in academic writing by analyzing 7,953 AI usage declarations from journal publications. AI adoption increased from 62.6% (October 2023) to 78.2% (March 2025), approaching a projected 85% saturation level. Physical and Social Sciences show highest adoption rates, while Health and Life Sciences lag behind. ChatGPT dominates across all disciplines (67-75% of usage), with disciplinary preferences emerging: multidisciplinary research favors writing tools while Physical Sciences utilize more translation tools. Language-related functions comprise 80-90% of all usage, with discipline-specific emphasis patterns. Network analysis reveals Physical Sciences exhibit the most diverse tool ecosystem, with ChatGPT serving as the central hub across fields. This first comprehensive cross-disciplinary analysis of actual AI usage patterns contributes valuable insights for academic publishing policies and discipline-specific AI literacy development.

Citations are central to the propagation of scientific information. Ensuring the accuracy of citations is essential to maintain the credibility of scientific knowledge. However, assessing citations is a significant challenge, especially at scale. This study examines the utility of natural language processing (NLP) in identifying poor citation practices. Specifically, we replicate Greenberg’s 2009 study on citation distortions in Alzheimer’s research, which demonstrated how poor citation practices can contribute to the establishment of unsubstantiated claims as facts. We explored two approaches: one that utilizes large language models (LLMs), and another that relies on existing publicly available NLP tools and publication metadata. Our findings suggest that, among Greenberg’s three types of citation distortion – citation bias, amplification, and invention – current NLP tools are most effective at detecting amplification, with more limited success in replicating Greenberg’s results with NLP for the other citation distortion types. Further refinements to LLM pipelines are needed to better capture the subtleties of citation bias and invention in biomedical publications.

While research misconduct poses a serious threat to science, the role of data management in preventing misconduct, particularly by ensuring that raw or primary data is securely archived and accessible for audits or verification, remains underexplored. This study examines the existing and potential relationships between research misconduct and data management practices.

Data practices vary widely across scientific disciplines. While Big Data has significantly transformed research activities across various domains and has been heralded as a revolutionary force in scientific paradigms, its application has not been uniform across all fields. This study examines Big Data research and practices in data-intensive scientific domains, identifying its distinct features and revealing the uneven adoption and impact of Big Data across disciplines. Our findings indicate that discussions on the epistemological concepts and definitions of Big Data in data-intensive scientific domains are limited, with little divergence among scholars. Machine learning emerges as a central technological focus across disciplines, closely integrated with research topics and widely driving scientific advancements. Additionally, this paper highlights the instrumental role of Big Data in scientific inquiry and underscores the disparities in its impact across different disciplines. Through this review, we aim to foster a more comprehensive understanding of Big Data’s evolving role in science, emphasizing the need for continued critical reflection as its influence continues to develop.

In the era of globalization, academic resources flow around the world. The cross-border flow of traditional academic publications such as e-journals and databases has received widespread attention and has formed basic flow rules. As an emerging and important academic resource, research data also travels across borders. However, most of the research focuses on how to regulate the cross-border flow of research data, and very few addressed statistical analysis of the cross-border flow of research data. This paper aims to study Chinese and the US research data repositories and illustrated the cross-border flow and multi-national cooperation of research data among the research data repositories, using proportion analysis, word frequency analysis, and social network analysis. This paper found that the degree of data localization in China and the US is high. Among the flowing research data, the US is more diverse in terms of flow direction, subjects and cooperative relationships.

The increasing volume of social media content surpasses the capacity of human moderation and poses psychological risks, leading to a need for automated moderation systems. However, these systems often exhibit biases against minoritized groups. One way to mitigate these biases is by altering the training data, which are biased by human annotators. Increasing diversity among annotators can help, but implementing this is challenging for machine learning specialists and tends to focus on minimizing identity-based bias rather than embracing diverse perspectives. Using moral systems theory from social psychology, we suggest that automated systems should incorporate diverse, context-aware interpretations of harm, embracing biases to adequately address moderation issues. We analyze how different dimensions of 2,180 U.S.-based annotators’ personal moral systems like institutional affiliation (religion, political party), values (political ideology), and identities (age, gender, sexual orientation, and race, ethnicity, or place of origin) influenced how they judged whether 101 social media comments were harmful. We find institutional affiliations have the greatest impact on labeling, followed by values and identities. These insights advocate for a diversity approach that reflects community-specific user bases, allowing model developers and online communities to intentionally select biases for better moderation outcomes.

Despite the growing body of makerspace services and programming in public libraries, research focused on designing accessible makerspaces and making programming for youth with disabilities has been limited, with a few exceptions. By analyzing data from various sources including field observations and interviews with staff and patrons from four public library makerspaces in the United States, this collective case study presents findings of how these makerspaces designed their spaces and what strategies were used in preparing tools and machines, online website, and marketing to enhance accessibility and inclusivity for youth with disabilities. Our findings provide practical implications for the universal and user-centered design of affordances in public library makerspaces.

Since September 2021, following the Taliban’s resurgence, girls in Afghanistan above the sixth grade have been barred from attending school and educational institutions, which effectively prohibited their access to formal education. This led to online schooling becoming their primary educational outlet. At the same time, there have been a few studies on Afghan girls' online education, and no study has yet investigated the effectiveness of their online schooling. Using a mixed-methods approach, this research assesses the effectiveness of online programs from the perspectives of students, parents, school leaders, and international organizations. Our study found generally positive perceptions of curriculum quality, collaboration in online learning environments, and the availability of online educational resources. However, issues such as limited access to internet connection and technology devices, unreliable electricity, and the lack of official diploma recognition emerged as significant barriers. The findings underscore the critical role of online libraries and resources in supporting online education. The study provides recommendations for human rights advocates and educational activists to enhance the viability of online schooling as a temporary solution for girls in Afghanistan. Our primary contribution is an information behavior model in the context of online education for girls in Afghanistan.

With the increased usage of Artificial Intelligence (AI) and tools like ChatGPT, various disciplines, including instructional design (ID), are experiencing rapid changes to organizational identity, pedagogy and practice. Thus, it is essential to understand how AI impacts the ID community and how the community reacts and adjusts to the change in practice brought in by AI-driven initiatives and workflows. Motivated by this social premise, we explored a sample of 100 conversations focused on AI from a popular subreddit (r/instructionaldesign) to understand community perceptions, learning visions and implications for practice. Our initial exploration highlights three key themes associated with the need for expanding curriculum and training, understanding the impact of practice and the evolution of disciplinary norms. These insights spark the need to understand how the ID field is evolving, embracing, and realigning disciplinary values to appropriate the usage of AI within the practice of ID. The long-term implications of this work include understanding the usage of online communities as forums of informal on-demand learning and the varied sociotechnical affordances that shape and regulate community building through these online formats, ultimately impacting the sustenance and efficacy of such virtual forums of learning and professional development.

Documentation of datasets is a longstanding concern for data curation and research data management. Additionally, work in AI ethics, fairness, accountability, and transparency has taken on the challenge of documenting and describing datasets used to train or test machine learning models, though with few overlaps or points of intersection with information science approaches. In order to foster increased conversation and collaboration across fields, I aim here to both assess the current landscape of AI’s data documentation frameworks and understand where shared interests with RDM might be possible and fruitful. I analyze four prominent frameworks for documenting AI datasets, considering: (a) their goals, influences and precedents; (b) formal qualities of their materiality, and, (c) noting where and how each framework has been adopted and applied. Results reveal some common features of documentation frameworks, as well as diverging goals and constraints. I close by reflecting on ways that information science might learn from these approaches stemming from AI to inform future work on documentation of datasets for scientific research and beyond.

A well-prepared abstract can help researchers in finding their needed resources by succinctly presenting main points of the study in the paper. However, it is a time- and effort-consuming task to create a quality abstract, which captures important key points of the full manuscript. In this study, we aimed to explore the possibility of using LLM AI as a tool to support authors who would like to draft a quality abstract for a research paper. We compared semantic similarities of abstracts that were prepared by the authors, generated with LLM AI, and the full-text content of 120 papers from ASIS&T 2024 conference. Findings include that different prompt engineering techniques did not generate semantically different abstracts, meaning that the baseline prompts performed well possibly due to the advancement of LLM AI models. Also, experts preferred AI-generated abstracts over the authors’ abstracts when there was semantic discrepancy between the two types of abstracts. This may indicate the usefulness of LLM AI as a tool to support human authors, who may be struggling to draft a quality abstract of their research manuscript.

Drawing on a global bibliographic corpus covering more than 23 million papers and 10 million disambiguated authors, we present the first longitudinal, institution‑level portrait of how COVID‑19 perturbed research activity and collaboration. Using multilevel regression and interrupted‑time‑series analysis, we trace participation, productivity, and collaboration for researchers at the 1,000 historically most‑productive universities prior to 2020, stratified by geography, field, career stage, and gender. Publication counts and co‑authorship networks surged in 2020, signaling an unexpected, rapid mobilization and resilience of the research system. Yet by late 2022 these metrics had reverted to their pre-pandemic trajectories, indicating that the spike was a short-lived reprioritization rather than a lasting shift. The lag inherent in many experimental pipelines – especially wet‑lab science – raises the prospect of delayed losses not yet visible within our time frame. Our study establishes an evidence‑based baseline for monitoring longer‑term effects and offers actionable insights for science‑policy makers seeking to safeguard research capacity during future global crises.

Our goal is to advance our empirical understanding of the direct engagement of knowledge workers in academia with generative AI (Gen AI), as they are the thought leaders in our society. We interviewed 17 knowledge workers, including faculty and students, to investigate the social and technical dimensions of Gen AI from their perspective. Knowledge workers expressed worries about Gen AI undermining trust in the relationship between instructor and student and discussed potential solutions, such as pedagogy readiness, to mitigate them. Additionally, participants recognized Gen AI’s potential to democratize knowledge by accelerating the learning process and act as an accessible research assistant. However, there were also concerns about potential social and power imbalances stemming from unequal access to such technologies. Our study offers insights into the concerns and hopes of knowledge workers about the ethical use of Gen AI in educational settings and beyond, with implications for navigating this new landscape.

Creative work is economically and socially important, information demonstrably plays a crucial role in creativity. Creative blocks are emotionally and professionally difficult for those engaged in creative work, and disruptive of the work itself. Despite the importance of resolving blocks in creative work and the well-known role information plays in supporting creativity, little research has examined the role of information in addressing (or causing) creative blocks. In this paper, we present a dedicated empirical examination of this role of information in creative blocks—to our knowledge the first of its kind. Our results show that information interaction and behaviour are key to resolving creative blocks. Traditional information behaviours, notably browsing, are key, but these are supported by a range of digital interactions, including scrolling, algorithmic curation, and easy digital curation of multimedia. Our paper cements the importance of both information behaviour and digital information interactions to creative unblocking.

While there is increased interest in studying the information behavior of individuals engaged in creative pursuits, the body of research on the topic is still small in comparison to the volume of studies concerning the information behavior of scientists, social scientists, and humanists. Two possible and interrelated explanations for this are 1) non-artists’ misunderstanding artists’ need for information as a support and inspiration for their work; and 2) the difficulty inherent in studying the creative process, which is highly personal to the artist and often not visible to others. This short paper describes a model for data collection designed to mitigate these challenges; by shifting the preliminary data collection to the artists themselves, diary studies mitigate the challenges presented by attempting to study artists’ information behavior in the internal and non-linear processes of creation. The author designed a study to capture 21 poets’ impressions of the role of information in both inspiring and supporting their poetry writing. This paper describes the project design and execution, data collection, and next steps in the author’s data analysis process.

The growing use of artificial intelligence (AI) in scientific research has raised concerns about “AI dependency”, a phenomenon that remains conceptually ambiguous and underexplored. Guided by self-regulation theory, this study proposes a four-quadrant typology of AI dependency based on goal orientation and self-efficacy. Semi-structured interviews with 20 researchers revealed four distinct patterns: collaborative active, instrumental active, passive compensatory, and passive pathway. Researchers with high goal value and high self-efficacy (collaborative active) treat AI as a knowledge collaborator while maintaining autonomy. Those with high self-efficacy but low goal value (instrumental active) prioritize efficiency and treated AI as a pragmatic tool. In contrast, those with high goals but low self-efficacy (passive compensatory) relied on AI to compensate for skill gaps, while individuals low in both dimensions (passive pathway) exhibited habitual dependence and emotional distress when AI was unavailable. These findings reveal the complex psychological and behavioral dynamics underlying AI dependency, offering a more nuanced conceptual understanding and informing interventions that promote critical, self-regulated AI use.

With the ongoing advancement of big data and AI, automatic extraction of knowledge units from ancient Chinese texts (ACTs) has become a key focus in Chinese natural language processing. However, existing solutions often suffer from limited task coverage, inadequate quality evaluation, and challenges posed by the unique linguistic features of ACTs. These factors collectively hinder the broader adoption of intelligent ACT processing systems. To address these issues, we proposed a multi-task semantic annotation and generation system, named “KnowACT”, which includes a data loading layer, a task processing layer, and a result output layer. Our preliminary experiment has shown that when compared to other advanced annotation systems, KnowACT has significant advantages in the aspects of task functional integrity, annotation efficiency, and quality control of annotation texts. It is believed that KnowACT can promote the development of knowledge extraction technology and relevant systems for ACTs.

In this study we investigate the use of large language models (LLMs), specifically ChatGPT, for structured deductive qualitative coding. While most current research emphasizes inductive coding applications, we address the underexplored potential of LLMs to perform deductive classification tasks aligned with established human-coded schemes. Using the Comparative Agendas Project (CAP) Master Codebook, we classified U.S. Supreme Court case summaries into 21 major policy domains. We tested four intervention methods: zero-shot, few-shot, definition-based, and a novel Step-by-Step Task Decomposition strategy, across repeated samples. Performance was evaluated using standard classification metrics (accuracy, F1-score, Cohen’s κ, Krippendorff’s α), and construct validity was assessed using chi-squared tests and Cramér’s V. Chi-squared and effect size analyses confirmed that intervention strategies significantly influenced classification behavior, with Cramér’s V values ranging from 0.359 to 0.613, indicating moderate to strong shifts in classification patterns. The Step-by-Step Task Decomposition strategy achieved the strongest reliability (accuracy = 0.775, κ = 0.744, α = 0.746), achieving thresholds for substantial agreement. Despite the semantic ambiguity within case summaries, ChatGPT displayed stable agreement across samples, including high F1 scores in low-support subclasses. These findings demonstrate that with targeted, custom-tailored interventions LLMs can achieve reliability levels suitable for integration into rigorous qualitative coding workflows.

Providing subject access to information resources is an essential function of any library management system. Large language models (LLMs) have been widely used in classification and summarization tasks, but their capability to perform subject analysis is underexplored. Multi-label classification with traditional machine learning (ML) models has been used for subject analysis but struggles with unseen cases. LLMs offer an alternative but often over-generate and hallucinate. Therefore, we propose a hybrid framework that integrates embedding-based ML models with LLMs. This approach uses ML models to (1) predict the optimal number of LCSH labels to guide LLM predictions and (2) post-edit the predicted terms with actual LCSH terms to mitigate hallucinations. We experimented with LLMs and the hybrid framework to predict the subject terms of books using the Library of Congress Subject Headings (LCSH). Experiment results show that providing initial predictions to guide LLM generations and imposing post-edits result in more controlled and vocabulary-aligned outputs.

In this project, we semantically enriched and enhanced the metadata of long text documents, theses and dissertations, retrieved from the HathiTrust Digital Library in English published from 1920 to 2020 through a combination of manual efforts and large language models. This dataset provides a valuable resource for advancing research in areas such as computational social science, digital humanities, and information science. Our paper shows that enriching metadata using LLMs is particularly beneficial for digital repositories by introducing additional metadata access points that may not have originally been foreseen to accommodate various content types. This approach is particularly effective for repositories that have significant missing data in their existing metadata fields, enhancing search results, and improving the accessibility of the digital repository.

While there has been research on the development of Indigenous subject headings and vocabularies, less research and development has focused on creating community-focused workflows to build in systems of community input and collaboration for vocabulary, working with communities to create policies, protocols, and digital tools to enable approval, usage details, and ongoing checks on vocabulary terms and technical infrastructure to create, store, and make vocabulary accessible, as well as the governance and protocols required for appropriate and respectful access at scale. This paper discusses preliminary research outlining initial methods of participatory research design and community collaborations for a platform for Indigenous structured vocabulary within Canada.

This study explores the distinct information needs of urban and rural populations by analyzing six months of Q&A posts on Naver’s Knowledge-iN in South Korea. Using Latent Dirichlet Allocation (LDA), KoBERT, and Non-negative Matrix Factorization (NMF), we compared major themes within urban and rural posts. Our findings show that both groups share interests and concerns regarding dental healthcare, transportation, education, and food. Urban posts emphasized daily life services and mobile technology, reflecting interests in convenience and connectivity. In contrast, rural posts focused on regional welfare, local spots, and family or emotional concerns, suggesting possible service gaps and unique social dynamics. Topic distributions varied across the three topic modeling methods: LDA revealed broader categories, NMF highlighted more specific segments, and KoBERT captured context-rich, nuanced themes. Overall, this comparative analysis underscores region-specific information needs and demonstrates the complementary benefits of multiple topic modeling techniques for understanding social and digital inequalities.

This study investigates the informational functions of queer spaces and how their spatial characteristics shape information access, flow, and sustainability. Using semi-structured interviews with 15 US queer adults who experienced the loss of a queer-focused information space, our analysis furthers information science theorizing by examining these spaces through the lenses of information grounds, small worlds, and boundary publics. Findings reveal that queer spaces are vital sites for identity work, community support, and information circulation. Information grounds serve as accessible, queer-adjacent entry points; small worlds foster queer information literacy but may reproduce exclusion; and boundary publics offer hybrid, adaptive alternatives when spaces become inaccessible. We find recurring patterns of spatial vulnerability, insider/outsider dynamics, and the centrality of information to queer visibility. We contend that designing sustainable queer information environments in the face of platform regulation, gentrification, and sociopolitical hostility, lends insight into the embodied and relational dimensions of queer spatial practice.

Scientific progress hinges on the interplay between collective intelligence and transformative paradigm shifts, yet predicting these revolutionary events remains a persistent challenge. This study introduces a novel human-aware AI framework that integrates the evolution of knowledge structures with the social dynamics of scientific communities to forecast groundbreaking innovations. Leveraging graph convolutional neural networks (GCNNs), we construct a hybrid higher-order network that unifies a domain knowledge graph—derived from millions of scientific publications—with a scientist collaboration-competition space, capturing both cooperative and competitive interactions among researchers. This approach quantifies collective intelligence by generating embeddings that reflect the intricate relationships between knowledge content and human agency. By analyzing thematic knowledge distances and social proximities within this integrated network, we identify pairs of scientific domains poised for disruptive convergence. Dynamic analysis of these embeddings further enables temporally precise predictions of paradigm shifts. Applied to the life sciences, our framework successfully aligns with historical milestones, such as Nobel Prize-winning discoveries, demonstrating its predictive power. This work offers a scalable, interpretable tool for anticipating scientific revolutions, bridging the gap between knowledge evolution and social dynamics, and providing actionable insights for fostering innovation across disciplines.

This study conducts a visual analysis of 491 participant-generated drawings to examine how individuals with varying cybersecurity expertise conceptualize security through visual metaphors and narrative strategies. Using a codebook grounded in visual rhetoric theory, we identified a striking consistency in imagery—particularly the recurring use of locks, shields, and oppositional symbols—across all groups. Findings suggest the existence of shared visual ontologies shaped by both common sense and collective stereotypes. We discuss the implications of these visual conventions for cybersecurity communication and propose future research integrating generative AI tools, audience analysis, and domain knowledge frameworks to uncover underrepresented conceptual gaps.

Artificial intelligence (AI) has been proposed to overcome distributed team cognition and information challenges (e.g., volume, velocity) in intelligence analysis. However, before deploying AI in the workplace, designers should evaluate the effects of proposed AI in realistic simulated environments, also known as testbeds. We conducted interviews with intelligence professionals which, combined with our research needs, resulted in requirements, designs, and the creation of the Human-Agent Teaming on Intelligence Tasks (HATIT) testbed. HATIT includes a web-based software platform, a shift handover intelligence task in a fictional world with 427 pages and 60 documents, and an initial, static AI agent called “Illuminate” that summarizes documents and provides social media topic models. HATIT enables controlled experiments in a realistic simulation. This testbed lets us evaluate the effects of different AI on perceptions (e.g., trust, workload), problem solving and team cognition, and information search before deploying in actual work settings with real consequences.

This short paper delineates an understudied theoretical development of information. Whitehead, a pre-cybernetic philosopher and mathematician, provided a conceptual schematic for hylomorphic information: information that does not dissect the mind from the body. His information suggests connections in ontology, phenomenology, epistemology, and ethics. Contemporary Whiteheadian literature theorizes connections between philosophy, science, and morality through hylomorphic approaches to organization, science and technology studies, and critical data research, suggesting extensions to cybernetic theory and artificial intelligence. This short paper provides a first interpretation of these relations and forwards directions information studies might find more immediate applications.

Extant research lacks comprehensive identification of distinct AI adopter groups necessary for targeted educational interventions to enhance AI literacy and mitigate the AI divide. Grounded in Social Cognitive Theory (SCT), this study categorizes AI adopters based on social cognitive characteristics, they are, fear of missing out (FoMO), AI attitudes, and self-efficacy. Further, we profile these groups by AI literacy and educational backgrounds. A survey of 620 participants using K-means clustering revealed three adopter types: (1) observers, characterized by lower education, AI literacy, FoMO, AI attitudes, and self-efficacy; (2) seekers, with intermediate educational levels, high FoMO, and strong AI literacy; and (3) professionals, highly educated individuals with low FoMO but high AI literacy. Findings demonstrate how educational disparities shape AI literacy through social cognitive factors. Theoretically, this research introduces an innovative SCT-based classification of AI adopters, offering practical insights for policymakers and educators to design tailored interventions addressing the AI divide.

As the use of generative artificial intelligence (GenAI) systems grows in daily life, there is a need to assess how users interact with these tools in socially and ethically informed ways. This study introduces a multidimensional scale to measure socio-ethical AI engagement competencies, reflecting users’ ability to evaluate, interpret, and ethically use AI-generated content, and to critically consider its broader social impacts and power dynamics. Drawing from interdisciplinary frameworks in AI literacy, self-efficacy, and AI ethics, we constructed an initial item pool related to social and ethical engagement.

Responses from 200 participants to an 18-item instrument were analyzed using Exploratory Factor Analysis (EFA) to capture four dimensions: critical appraisal, critical comprehension, ethical behavior, and anthropomorphic interaction. The scale demonstrated strong internal consistency. This work contributes to a theoretically grounded and empirically supported instrument for assessing critical and ethical engagement with GenAI, which has implications for AI literacy, responsible technology use, and curriculum design.

This paper proposes and validates a model that describes the influences of cognitive appraisals, including threat appraisal, coping appraisal and message appraisal of information integrity on phishing susceptibility, and explore threat detection as a mediating mechanism underlying their effects. Threat detection is crucial particularly as AI makes phishing more difficult to identify. An online scenario-based survey involving an attack scenario using a phishing email with 313 participants was conducted to validate the proposed model. Findings reveal that threat detection and perceived severity are associated with reduced phishing susceptibility, while perceived vulnerability and perceived information integrity increase it. Although self-efficacy predicts threat detection, it does not directly reduce responses to phishing messages. Additionally, threat detection mediates the effects of perceived severity and perceived information integrity. This paper contributes to phishing research by integrating information security behavior and message-related literature. It underscores the importance of user education, emphasizing online literacy and cautious engagement with unsolicited messages to counter phishing threats.

This study investigates the relationship between public library accessibility and local extinction risk in South Korea. Using data from 229 municipalities from the years 2013, 2018, and 2023, the study analyzed the number of public libraries, service coverage, and accessibility. Spatial analysis was conducted using QGIS, and correlation analysis was conducted using SPSS. Results show a general improvement in accessibility, yet areas at higher extinction risk remain underserved. A statistically significant correlation between the extinction risk index and library accessibility was observed across all years, suggesting an increasing degree of spatial inequality. Despite national efforts to expand infrastructure, vulnerable regions continue to face limited access. As public libraries are essential for digital literacy and social inclusion, especially in the AI era, spatially equitable policy interventions are needed to reduce information inequality and support regions at risk of local extinction.

Generative AI is transforming how youth interact with technology, introducing novel risks that are not fully understood by current safety frameworks. This poster presents an empirically grounded taxonomy of risks youth face in interactions with generative AI, based on analysis of 344 chat transcripts, 30,305 Reddit discussions, and 153 real-world AI incident reports. We identify six main risk domains—mental wellbeing, behavioral and social development, toxicity, privacy, bias and discrimination, and misuse or exploitation—encompassing 84 specific risks across four interaction pathways. We highlight GAI-unique risks such as emotional dependency, blurred boundaries between virtual and real life, and harmful GAI-initiated behaviors. Our taxonomy offers a structured foundation for practitioners, educators, and policymakers to recognize and mitigate these emerging threats and bridge critical gaps in existing approaches to youth online safety and AI risk.

This study examines the effectiveness of generative AI-driven personalised nudges in reducing students’ procrastination during informal learning, involving thirty-five participants in a 14-day diary study. Nudges, delivered as notification messages via the mobile application, were triggered based on generative AI analysis of participants’ routine activities to encourage their learning. The pre- and post-intervention measures were administered to assess the outcomes of the nudging intervention. Results show significant differences between pre- and post-intervention, suggesting the positive influence of nudges and their supporting role in participants’ learning. The findings provide insights into how integrating generative AI-driven personalised nudges into digital environments has the potential to transform the learning experience and enhance the effectiveness of these nudges.

This study empirically explores how Natural Language Processing (NLP) and Computer Vision (CV) datasets and models are reused in the Hugging Face community. We find that NLP tasks - such as Zero-shot-classification, Sentence-similarity, and Feature-extraction - require more diverse datasets compared to CV tasks on average. On the other hand, NLP datasets were reused less frequently than CV datasets. In addition, CV models were reused frequently to develop other models compared to NLP models. In conclusion, NLP models reused diverse datasets for training, while CV datasets and models were reused more and layered up together to develop other models. This study contributes to the understudied area of dataset and model reuse in computing and the broader data reuse subfield under Information Science.

This study experimentally reproduces the methodology of Akgul et al. (2022) on influencer VPN advertisements, adapting it to a Taiwanese context while integrating extensive generative AI (GAI) support. We developed a three-stage GAI assisted pipeline: transcription using OpenAI's Whisper, segment identification via prompt-based querying with Google’s Gemini API, and performance assessment against human annotations. This hybrid human-AI workflow reduces the manual screening burden while preserving analytical rigor, allowing researchers to concentrate on interpretive analysis. The study demonstrates how qualitative research can evolve through experimental integration of AI tools, augmenting human expertise rather than replacing it—particularly in culturally specific domains such as cybersecurity communication in Taiwan.

This study explores how ovarian cancer (OvCa) patients and caregivers engage with online health communities (OHCs), offering insights to address the cold-start problem in health recommender systems. Analyzing 909 initial posts and 14,816 comments from the National Ovarian Cancer Coalition (NOCC) forum, we examined how users’ contributions evolve over time. Initial interactions typically center on emotional support and personal experience sharing, while more advanced engagement, such as advice and referrals, emerge later as users gain more domain knowledge. We introduce a novel “start position” metric to identify when users begin providing each support type. Our results reveal distinct progression patterns and emphasize the importance of dynamic user modeling to reflect users’ evolving knowledge and roles. These findings inform the design of OvCa-specific recommender system, which our team develops with support of the National Library of Medicine.

As part of a larger project to develop a generative artificial intelligence (GenAI) literacy framework and toolkit for higher education, this poster presents an analysis of seven literacy frameworks relevant to GenAI literacy in academic contexts. Through thematic analysis, we identified 10 competencies essential for GenAI-literate students in higher education. These competencies are organized into four progressive stages: (1) understanding GenAI at conceptual and technical levels and its capabilities and limitations; (2) using GenAI tools purposefully, effectively, and appropriately; (3) analyzing and evaluating various GenAI models and tools for performance and societal, ethical, and legal impacts, and (4) customizing or creating GenAI tools to address specific needs and generate innovative applications or theoretical insights. We discuss key implications of the results and next steps for developing a GenAI literacy framework for higher education.

A wide range of studies has explored historical events and their long-term impacts, with urban redevelopment, particularly in the contexts of urban renewal, gentrification, and redlining, emerging as a rich area of research. However, despite extensive attention to its causes and consequences, quantifying how urban structures have changed over time remains methodologically challenging, as scanned historical maps contain visual noise and annotation. While some computational methods have been developed to quantify street networks, they are often limited in applying to historical maps and their differences. This study proposes a computational method to measure urban transformation by comparing contemporary road structures with those from historical maps. Using the 1930’s redlining maps as a case study, we extract road features and quantify urban structural change over time. The preliminary analysis sheds light on studies focused on urban transformation and redevelopment, aiding researchers and policymakers in understanding the spatial legacy of historical policies.

This paper investigates how X is used for social movement campaigns related to surveillance, technology, and immigration. This paper provides an exploratory analysis of the corpora of tweets (n = 60,602) engaged in the #NoTechForICE social movement campaign from 2018 to 2023. This research aims to address how users make sense of the hashtag #NoTechForICE by conducting a computational approach featuring topic modeling and qualitative analysis. Results identified six themes that demonstrate a baseline characterization of the X discourse of users engaged with #NoTechForICE: a) Calls to Action, b) Ethical Data Use, c) People Over Profits, d) Movement Recruitment, e) Collective Demonstrations, and f) Movement Refusal. This work offers an entry point for further research into efforts among advocacy groups to address policy concerns related to AI and emerging technologies by federal immigration enforcement agencies.

Accurately recognizing clinical research articles is critical for research evaluation and evidence-based decision making, yet traditional metadata-based approaches often lack semantic precision. In this study, we propose an automatic classification framework that utilizes pre-trained language models (PLMs)—BERT, BioBERT, and BioMedLM—to distinguish between clinical and non-clinical biomedical articles. Experiments were conducted on a manually annotated dataset of 20,000 articles to ensure reliability and precision. We designed and compared three input strategies along with a summary refinement approach. The results show that BioBERT using refined abstracts achieved the highest performance (F1 = 0.917, AUC = 0.970), demonstrating the advantages of domain-specific pre-training and content-focused input design. This work provides a scalable and interpretable solution for categorizing medical literature.

Purpose: Ancient Chinese poets play a key role in literary history. Structuring their biographical knowledge from a multi-dimensional perspective supports deeper exploration and digital applications in classical literature.

Methodology: We construct an event-centered ontology for poets, covering: (1) basic information, (2) dynamic life events, and (3) evaluative discourse. Linked data enriches connections between life events and literary works with spatiotemporal context. A biographical knowledge graph is then built and analyzed using GIS, visualization, and social network analysis.

Findings: Case studies of Li Bai and Du Fu show that both had wide-ranging geographic trajectories and close ties to major cultural centers. Du Fu's network was centered on officials, while Li Bai’s was more diverse, including Daoists. Emotional analysis reveals a consistently melancholic tone in Du Fu's poetry, while Li Bai’s emotional expression fluctuated, reflecting both joy and sorrow

This paper proposes Humanities-in-the-Loop, a methodological framework that embeds close reading into each stage of the Retrieval-Augmented Generation (RAG) pipeline to enhance the processing of digital archival materials. This framework includes manual annotation, knowledge maintenance, reviewer validation, prompt engineering, answer evaluation and human interpretation. Taking the diaries of Coching Chu as a case study, the system addresses the limitations of conventional RAG methods in capturing the contextual complexity and historical nuance inherent in personal archives. The proposed framework not only enhances answer accuracy and interpretability but also enables traceable, human-centered inquiry in digital humanities research.

With large language models (LLM) increasingly in the spotlight, their approach to censorship on topics like immigration and conflict deserves a closer look. Our research investigates the role of censorship in LLMs, and how these models manage controversial topics. We explore the differences between acontextual and contextually prompted models, examining their responses to subjects surrounding immigration policies and international conflicts. While existing literature highlights LLMs’ ability to maintain fairness, there is a definitive gap in understanding how contextual prompting influences model responses and potential censorship mechanisms. Through a methodology involving systematic and contextual prompting, we reveal that contextually prompted models often deliver more nuanced responses, potentially bypassing strict moderation due to their evaluative nature. This study contributes to the ongoing discourse on AI ethics by offering insights into improving LLM design to balance objectivity and usability, ultimately informing policy guidelines for deploying AI in sensitive domains.

Persistent identification of scholarly entities is an ongoing challenge in the web environment. This poster reports on a project focused on understanding the purposes and benefits of persistent identifier (PID) assignment for research facilities and instruments. We present findings related to PID assignment practices, motivations, and use cases for stakeholders based in scholarly research institutions, as well as challenges related to facility and instrument evolution, granularity, and relationships. Overall, we demonstrate that diverse practices currently exist regarding how to assign PIDs to research facilities and instruments, and few broad conventions or norms have emerged.

This study examines the role of libraries in Japan’s Basic Plan for Revitalizing Central Urban Areas and investigates the functions and values emphasized in regional revitalization policies. Drawing on Yamagishi’s (2024) five dimensions of public library legitimacy, namely democracy, communication and education, history and culture, economy, and librarianship, a qualitative content analysis was conducted on 288 plans submitted by 157 municipalities certified between 2006 and 2024. The findings indicate that libraries are particularly valued for promoting intellectual freedom, offering multifunctional community spaces, and enhancing economic vitality by attracting a diverse range of users. Although cultural activities and support for lifelong learning are recognized, less attention is given to the preservation of local history and traditions. Moreover, values such as neutrality, the public sphere, social justice, and the professional expertise associated with librarianship receive only limited emphasis. The study underscores the importance of developing future urban policy in Japan that more comprehensively addresses the multifaceted legitimacy and wider societal contributions of public libraries beyond the economic dimension.

While institutions have developed Trustworthy AI (TAI) frameworks, less is known about whether the public indeed finds TAI factors important to increasing their generative AI (GAI) usage, and how TAI and other usage motivators relate to GAI usage types and perceived GAI risks. Surveying 322 U.S. adults, this study found Technical Robustness and Safety to be the top TAI factor, though it was second overall (after Effort Expectancy). Multiple Correspondence Analysis (MCA) found notable groupings. For example, infrequent GAI usage was related to preferring the Privacy and Data Governance TAI. Respondents who perceived GAI as lower risk preferred the Societal and Environmental Well-being TAI, Performance Expectancy, and Facilitating Conditions motivators.

Searchers engaged in complex search tasks and following exploratory search processes often need to pause their search activities. Resuming such tasks is especially challenging in the context of multi-platform search, as previously discovered information may be dispersed across multiple search platforms. While a variety of approaches have been developed to support aggregated search, supporting cross-session searching within this context is understudied. Building upon prior work on aggregating search results within a digital humanities context (Europeana, our University Library, and Wikipedia), we propose a sensemaking approach to facilitate task resumption. This includes a cluster-based workspace, drag-and-drop cluster manipulation, interactive highlighting of relevant passages, and generative AI summaries that make use of the highlighted passages.

This poster presents a systematic review of 98 user studies in Generative Interactive Information Retrieval (GenIIR), exploring how users engage with GenAI in interactive information-seeking contexts. Guided by the Information Seeking in Context (ISIC) framework, we examined two key dimensions: Research Focus and Information Context. Our results show that most studies examined user perceptions and information behavior, while many also addressed system design evaluation. These studies covered diverse domains and user groups. The most common settings were general, health, and education domains. Participants were primarily general users or students, and these studies often involved public tools like ChatGPT. Other studies focused on professional domains and custom GenAI systems, where interaction context, user roles, and task environments were more specialized, highlighting ISIC’s emphasis on context-sensitive information behavior. These patterns reflect the accessibility of public tools and a rising emphasis on context-sensitive system design. This review provides insights into developing context-aware, human-centered GenAI systems.

Recent advances in generative artificial intelligence offer unprecedented opportunities to reimagine archival sources and deepen public engagement with contested histories. This study investigates how large-language-model (LLM) multi-agent simulations can transform oral-history interviews from China’s Cultural Revolution in Memories: The CR/10 Project into dynamic dialogues among artificial agents. Using the open-source tool TinyTroupe, we construct personas grounded in authentic testimony and embed them in themed conversation spaces focused on memory, reconciliation, intergenerational understanding, and historical responsibility. The resulting simulations enable audiences to observe how divergent personal narratives collide, converge, and evolve across generations, providing a richer appreciation of this complex past. Preliminary findings indicate that LLM-driven agents can convey multifaceted historical experience in a more immersive and interactive form than traditional interfaces.

This study examines whether the semantic meaning of Library of Congress Classification (LCC) class names provides additional insight beyond the system’s hierarchical structure for organizing knowledge. Using SBERT, a natural language processing (NLP) model for generating semantic embeddings, we investigate the relationship between the semantic meaning of LCC subclass names and the word usage patterns of the texts assigned to these subclasses. Our results show that although LCC subclass names with similar semantic meanings occasionally correspond to similar word usage patterns, there is no consistent relationship between the two. In contrast, whether two subclasses belong to the same main class reliably predicts the similarity of word usage in the texts assigned to them. While semantic embeddings of LCC subclass names offer intriguing possibilities, our findings indicate that the hierarchical structure of the LCC system remains more robust for knowledge organization.

This survey study investigates how digital humanists perceive and approach generative AI (GenAI) disclosure in research. The results indicate that while digital humanities scholars acknowledge the importance of disclosing GenAI use, the actual rate of disclosure in research practice remains low. Respondents differ in their views on which activities most require disclosure and on the most appropriate methods for doing so. Most also believe that safeguards for AI disclosure should be established through institutional policies rather than left to individual decisions. The study’s findings will offer empirical guidance to scholars, institutional leaders, funders, and other stakeholders responsible for shaping effective disclosure policies.

Archival and information science scholars have begun to evaluate the impact of AI tools on how archival objects are created, used, and evaluated (Shinde, Kirstein, Ghosh, and Franks, 2024). Researchers have addressed a broad range of questions, including the evidential properties of AI generated archives, the use of large language models in supporting archival processing, and strategies for evaluating the authenticity and reliability of AI generated archives (Jaillant and Caputo, 2022; Reducindo and Olague, 2024; Wagner and Blewer, 2019). This initial work represented in the poster indicates a need to investigate questions pertaining to the evidential foundations and societal impacts of AI generated and mediated archives, and the potential and existing requirements for AI tools to enhance use of legacy and born-digital archival sources. This is significant for supporting traditional archival objectives in providing for and assessing reliability and authenticity (Arias Hernández and Rockembach, 2025). These issues are present at multiple stages of the archival life cycle – records creation, access, and evaluation and use – and are introduced and discussed broadly in this poster.

This study investigates how disability providers engage in information provision within a fragmented disability service system. Through interviews with 61 providers from state, local, and nonprofit agencies from the state of Virginia, we identify two descriptive patterns of organizational information practices and five multi-level shaping factors: system disintegration, bureaucratic complexity, provider expertise, user technological readiness, and community trust. We show how these factors interact, producing layered strategies that providers develop to maintain and simplify information provision across fragmented and shifting service environments. These findings reframe information provision not as a linear transaction, but as a relational and adaptive practice shaped by institutional gaps and infrastructural complexity. The study contributes to ASIS&T by extending information grounds theory to organizational contexts, offering a grounded account of how disability providers manage and coordinate information work in disability systems.

Multimodal disinformation (MD) has become more widespread in social media, raising the importance of information credibility research in this context. Meanwhile, engagement cues like comments and virality metrics may influence online credibility assessment. Using a between-subjects experiment, we examined the main and interaction effects of multimodality, comments, and virality metrics on disinformation recognition. Our results unexpectedly revealed that MD is easier to recognize than unimodal disinformation. Further, multimodality could attenuate the impact of comments and virality metrics, making people more susceptible to disinformation. Based on these findings, we discussed the theoretical and practical implications.

This poster presents an analysis of data governance policies in higher education institutions and their libraries in the Big Ten Academic Alliance, a consortium of 18 large, research-intensive universities in the U.S. It critically explores how these institutions use student data, including library data, and seeks to develop a framework for ethical, user-centered data governance strategies. A policy discourse analysis was undertaken using 210 publicly-available data governance documents and 24 library privacy policies. These were examined for expressions of data ownership, permissions, protection, and autonomy, as well as for omissions that obfuscate the rights of students as data owners. None of the reviewed policies allowed students to consent to data collection or analysis, and only one covered the use of data by artificial intelligence. This work builds on information science and adjacent fields’ analysis and development of data governance practices, and examines how privacy is being reconsidered within academic librarianship.

Archivists often make music scores available online as PDFs, JPEGs, TIFFs, or other image-based file types. But, for some Disabled users, such image-based files can be inaccessible because they are incompatible with assistive technologies. So, using the Music Encoding Initiative (MEI), I devised a workflow to convert those image-based scores to accessible formats. This poster presents the results of a pilot study implementing that workflow on a set of 14 scores by Maria Theresia von Paradis to create Braille music notation (BRF), modified stave notation (MSN), and audio files (WAV). Experimentation and usability testing revealed that the workflow is successful but requires increased automation (especially optical music recognition, or OMR) to be realistically implemented across music archives.

This study investigates how the relationships between cities are represented in local news by analyzing co-mentions of cities in 31,004 news articles from Alabama. Using a large language model, we extract geographic references and construct co-mention networks that reflect both spatial proximity and symbolic connections. To interpret these links, we develop a classification framework of relationships between cities, including common impacts and sequential dynamics. Our preliminary analysis reveals that different news categories produce distinct patterns of spatial association. This approach offers a novel methodology and a new lens for understanding media-driven spatial imaginaries, contributing to research on information geographies and shedding light on the relational dimensions of local information.

This project explores how publicly sourced digital platforms, combined with generative AI, can support the study of cultural diaspora in the performing arts. Using ballet as a case study, we extracted data from 518 English Wikipedia entries and applied GPT-based models to structure information on premiere dates, locations, and creative contributors. We identify culturally significant locations and construct a bipartite network linking artists to ballet premiere cities, offering a framework to trace transnational creative flows that contribute to cultural diaspora. Our findings show that integrating human-curated data with AI-assisted structuring provides a valuable entry point for data-driven analysis of cultural mobility in understudied domains. This work contributes to growing efforts to bridge computational methods and the humanities through accessible and reproducible approaches.

Many libraries have AI pilots, such as Chatbots and automated metadata pipelines, but librarians often feel unsure about integrating these tools into everyday service. This study presents early results from a grounded-theory investigation aimed at defining what “AI readiness” means for libraries. A systematic review of 46 publications was combined with five pilot interviews to generate 145 open codes that were merged into a three-layer, fourteen-pillar framework. The technological layer highlights computing access, usable data, and a safe sandbox; the organizational layer includes explicit AI goals, leadership backing, staff AI literacy, and clear rules; the environmental layer includes privacy mandates, peer pressure, user feedback, public policy, and professional organization support. Interviews revealed gaps in data quality, bias checks, and user feedback routines, while leadership engagement and a culture of small-scale experiments appear as key factors. Our next phase will include interviewing at least twenty additional librarians, developing survey instruments based on the framework, and testing interactions among pillars. The framework aims to serve as a practical checklist for library managers to evaluate AI readiness and identify areas needing development before committing resources to full AI deployment.

This study employs survival analysis to examine factors influencing job departure among Library and Information Science (LIS) professionals, with a particular focus on professionals of color. It investigates how perceived fair treatment impacts retention, comparing experiences between professionals of color and their White counterparts. Findings indicate that perceived fair treatment is significantly associated with lower turnover rates among professionals of color. In contrast, this factor does not significantly influence job departure among White professionals. These results underscore the critical role of perceived fairness in workplace treatment for retaining LIS professionals of color. The study concludes by discussing implications for advancing equity, diversity, justice, and inclusion (EDJI) within the LIS field, offering insights for policies and practices aimed at fostering more inclusive and supportive work environments.

This article describes a study into the health and harm reduction information behaviors of people with opioid use disorder. The lead researcher traveled to the Kensington neighborhood in Philadelphia and conducted a survey with 106 participants that were using opioids, such as heroin and fentanyl, openly outside in a public library courtyard. The survey asked about their health information seeking, their feelings of self stigma, and compared these to metrics that are highly correlated with likelihood to overdose. Our findings are that people with a higher degree of internal shame or stigma about their drug addiction are less likely to ask for harm reduction information and are more likely to overdose. This points a clear path for stigma-reducing public health initiatives. Their responses to the survey indicate behaviors that are in alignment with Chatman’s insider-outsider effect theory, which indicates that any interventions may benefit from a community-based participatory approach.

Immersive analytics (IA) leverages technologies like virtual reality to support data exploration and decision-making by presenting large volumes of multimodal data in interactive 3D environments. However, the complexity of these systems can pose challenges for users, particularly during initial engagement. Effective onboarding is key to helping users acclimate and learn interaction techniques. This study evaluates three onboarding methods for VR-based IA systems: a desktop tutorial, in-VR instructor-led training, and self-guided exploration. Most participants preferred using all three and highlighted the need for early access to training components that require more time to understand.

Smart Home Internet of Things (IoT), traditional household devices equipped with technological intelligence to meet the needs of consumers who are increasingly aware of technology. These devices are finding a growing secondary market. However, the shortcomings of the instructions provided by the manufacturer for safe transfer of the device raise significant privacy concerns for consumers. This study uses a content analysis of guidance provided by manufacturers from selected publicly available documentation, including manufacturer websites, support pages, and manuals, to map the current state of information on privacy risks related to the second-hand smart home IoT. This study seeks to answer the question of how manufacturers are addressing the safe transfer and reset of smart home IoT. This study contributes by providing insights into the availability and clarity of the safety guidelines provided by IoT manufacturers to consumers of secondary smart home devices. It also serves as a guide for manufacturers to enhance consumer IoT privacy.

This paper presents the formation, mission, and early outcomes of the North Carolina Central University (NCCU) Institute for Artificial Intelligence and Emerging Research (IAIER) – the first AI research institute founded at a Historically Black College or University (HBCU). Established in January 2025 with $ 1 million in support from Google.org, IAIER represents a bold commitment to AI literacy, ethical research, and community engagement. We describe the development of programming such as Soaring Conversations and Seed Grants, share survey results from pilot learning sessions, and reflect on participant growth in understanding the interest in AI tools. This initiative models how underrepresented institutions can lead national conversations about artificial intelligence through inclusive, human-centered practices.

This paper presents a pilot study on integrating the use of Generative AI into maker-based STEM learning through playful activities for children (aged 6-12) and their families in public libraries. Our preliminary analysis of log data from an LLM-enabled chat system and video recordings shows how children and their caregivers used Generative AI for various purposes, such as seeking assistance when encountering challenges, through which they could also learn about how to formulate prompts for GenAI. We also found some limitations of the LLM-enabled chat system in providing situated feedback during maker-based learning. Our study sheds light on the potential of hands-on activities based on designing and making for children’s AI literacy as well as problem-solving, creative thinking, and prompt engineering.

Taxonomies organize concepts into broad categories consisting of more specific subcategories. In this study, we explore the use of multiple large language models (LLMs) to construct a domain-specific taxonomy. We propose a five-step workflow that combines LLM prompting, taxonomy alignment, and human validation. To test this workflow, we prompted six state-of-the-art LLMs to generate taxonomies with a maximum of two levels and 30 nodes. Although there were structural and syntactic variations, all models produced coherent taxonomies. Our findings suggest that even in the absence of ground truth data to facilitate taxonomy construction, integrating outputs from multiple LLMs can result in a reasonable starting point for a domain-specific taxonomy. In future work, we plan to complete the remaining steps in our proposed workflow by working with domain experts to verify the combined taxonomies; we will also test the generalizability of this workflow to other domains.

While Gen AI tools are rapidly reshaping how we learn, work, and create, little is known about how teenagers—digital natives yet non-professional users—perceive this emerging technology. To address this gap, this study conducted semi-structured interviews with 20 teenagers in China to investigate their understanding of Gen AI and used thematic analysis to identify common themes in their drawings and responses. We found that: 1) Teenagers' mental models of Gen AI can be divided into four categories: technological, procedural, functional, and comparative; 2) Mental models' characteristics include a high degree of technical trust, and a willingness to anthropomorphize Gen AI. These findings contribute to a better understanding of teens’ cognition about Gen AI, providing implications for AI literacy education of young people and the interactive design of Gen AI platforms and tools.

LGBTQIA+ individuals face challenges in accessing affirming health information, including discrimination, lack of culturally competent care, and digital resource availability. Resources created by communities and institutions can provide LGBTQIA+ audiences with this information, yet limited research has explored their scope and topics. This study examines the characteristics, subjects, accessibility, and inclusivity of 217 U.S.-based LGBTQIA+-focused online health resources. We identified these resources using Google and analyzed them using content analysis. Findings denote that while online LGBTQIA+ health resources broadly support mental health, crisis intervention, and transgender care, the latter often emphasize national resources over localized coverage. Resources minimally represented LGBTQIA+ groups like intersex and bisexual people as well as those with diverse intersectional identities. Further, accessibility and privacy features like escape mode and screen reader capabilities were largely absent. Relevant stakeholders, like information professionals, healthcare providers, and LGBTQIA+ organizations, can use findings to recognize gaps in online LGBTQIA+ health information networks and inspire resource creation.

Generative AI has lowered use barriers and expanded voice cloning’s capabilities. Questions about how voice cloning may impact our claim to ownership over our voices remain open, with legal standards lagging in protections for everyday people. This suggests there is a critical need for researchers to develop more models to understand the complexities of voice and voice data as emerging sites where extraction and exploitation can occur. We propose one such model that considers how voice cloning exposes tensions between understanding how voice functions in social versus commodity frameworks. We approach this topic through two main research questions: 1) What do different paradigmatic understandings of voice reveal about the power dynamics of voice cloning? 2) How is voice characterized in legal cases and discourse around voice cloning? Using legal cases as evidence, we investigate how different voice paradigms are materialized in court decisions and argumentation. We identify key cases where voice cloning is litigated as misappropriations of identity, demonstrating the incompatibility between the value of voice as an intrinsic property of the self and the value of voice as a product. Our framework provides theoretical footholds for describing voice cloning and suggests new paths forward for researchers and policymakers.

This paper explores how documentation shaped the early development and adoption of the FORTRAN (Formula Translating System) programming language. This study draws on documentality and genre theory to show how a variety of documents, such as reports, manuals, textbooks, and standards, worked together in support of the development of FORTRAN as a sociotechnical system. Through a diachronic genre analysis, this study traces when these documents were created and how they addressed the changing needs of FORTRAN programmers. The findings show that documentation played a constitutive role in recruiting programmers and stabilizing FORTRAN. By focusing on how multiple documents function as a genre set, this paper considers how documentation structured knowledge, shaped participation, and coordinated practices over time.

The Digital Empowerment Leadership Program (DELP) at North Carolina Central University (NCCU) takes on issues of digital inequity in underserved communities with a strong community-focused approach. By partnering with local groups, this program blends device distribution digital skills training, and shared decision-making to tackle access and adoption gaps. This write-up highlights lessons learned from DELP’s rollout, explains the program’s influence on marginalized people in Durham, North Carolina, and talks about obstacles faced and ways to keep such programs running. The results emphasize how crucial it is to assess community needs, adapt to changes, and maintain strong partnerships to achieve fair digital access.

Various factors have been reported to influence information disclosure on social media, yet social norms remain an understudied albeit potentially significant determinant. Guided by social norm theories, this study investigates the influence of perceived social norms on online sharing behaviors, alongside the moderating role of privacy concerns. A survey of 1,039 U.S. participants revealed strong associations between perceived social norms and both visual and written disclosure behaviors. On Facebook, both vertical and horizontal privacy concerns were found to weaken the effects of perceived descriptive and subjective norms on visual and written disclosure. However, despite high levels of privacy concerns, no significant moderation was observed between perceived social norms and disclosure on Instagram. These findings extend privacy literature by highlighting the robust influence of social norms on sharing behaviors and emphasize the need for further research on how such norms are perceived. This study offers theoretical contributions to Lapinski and Rimal’s Social Norm Theory and identifies avenues for future research.

Prostate cancer patients and caregivers often face overwhelming challenges in accessing reliable health information to support their treatment decision-making. This study aimed to assess the understandability, actionability, clarity, and readability of the information materials offered by an eHealth platform, the Interactive Propostate Cancer Information, Communication, and Support (iPICS) program. Using three validated health literacy instruments, we assessed a sample of the self-curated prostate cancer education materials across three formats on the iPICS platform. Descriptive statistics and testing demonstrated the distribution of the health literacy levels across three material types. The findings will inform and guide the ongoing enhancement of iPICS multimedia content and user experience, supporting patient education, informed treatment decision-making, and self-care management.

As public agencies increasingly adopt chatbots to deliver essential services, questions of equitable access and usability become critical. This study presents findings from an audit of chatbot services on U.S. state-level Supplemental Nutrition Assistance Program (SNAP) websites, with a focus on language accessibility and user experience. Using an inductive, qualitative coding approach, we evaluated chatbot visibility, language support, interpretive nuance, and privacy features across 56 websites. Only 11 states featured chatbots and only 4 of those supported Spanish. Drawing on information gatekeeping theory and information poverty theory, we argue that these design limitations disproportionately affect marginalized users, particularly non-English speakers. Our findings highlight the need for inclusive, multilingual chatbot design to ensure equitable access to public information. Recommendations include bilingual parity, improved interface visibility, and user-centered feedback mechanisms.

[Purpose/Significance] Nüshu is the world’s only script created and used exclusively by women, originating in the 19th century in Jiangyong County, Hunan Province, China. In the context of China’s national strategy for cultural digitalization, this study explores how the distinctive features of generative artificial intelligence—knowledge production, creative autonomy, and collaborative experience—can be harnessed to address the current limitations of Nüshu narrative models. [Design/Methodology] Drawing on interdisciplinary literature, this study examines the technical characteristics of generative AI and its relationship with mechanisms of interactive digital storytelling. [Findings/Conclusion] Based on the SPP (System–Process–Product) framework, this study proposes a new AI-driven interactive narrative model for Jiangyong Nüshu, structured along three dimensions: narrative system, narrative process, and narrative product. Additionally, this study outlines practical development strategies across three implementation aspects: data, technology, and human resources. [Originality/Value] Leveraging the creative and interactive potential of generative AI, this model provides a more dynamic and participatory pathway for Nüshu storytelling, emphasizing personalized creation and immersive engagement. The study offers new approaches to enhance public memory and cultural identity associated with Nüshu, and serves as a reference for the revitalization and sustainable development of intangible cultural heritage in the AI era.

This paper presents a design-focused approach to creating an AI-enhanced module that transforms archival photographs into dynamic virtual reality (VR) experiences. The project addresses the need for immersive access to cultural heritage while avoiding premature claims about historical accuracy. The Arizona Historical Society (AHS) serves as a testbed due to its metadata-rich photographic collection, institutional scale, and proximity. The system integrates open-source AI tools for depth estimation and mesh generation, guided by structured archival metadata. A participatory design methodology involving archivists, historians, users, and UX designers and AI developers informs ethical parameters and usability considerations. As a work in progress, the project contributes a replicable framework for heritage institutions interested in developing responsible, metadata-driven immersive systems grounded in archival values.

Scientific research frequently relies upon material samples (e.g. rocks, fossils, tissue samples, etc) as a primary element for reference and study. Across disciplines, particularly in the natural sciences, observations made on samples collected in the field or synthesized in the laboratory constitute a critical data resource. Nevertheless, the management of information about and derived from samples (e.g. handwritten field notes, photos, sub-sampled products, digital data products) remains a significant challenge. Proper curation and archival of this information is crucial to ensuring the reusability of samples, but little is known about what information is most important to researchers seeking to find and reuse samples. In this poster, we present preliminary results of an on-going study examining the “sample-seeking behavior” of researchers. Our work directly informs the improvement of sample database architecture and search user interfaces to support scientists in more efficiently discovering useful and usable samples and sample metadata.

This study explores how university students design single-turn prompts for academic learning tasks using AI tools. 19 students generated 57 prompts across exploratory, precise, and comprehensive scenarios. Using Bloom’s Taxonomy and thematic analysis, the study found exploratory prompts often lacked clarity, while precise prompts produced highly relevant responses. Comprehensive prompts supported higher-order thinking but varied in specificity. Content analysis showed Perplexity outperformed ChatGPT in research tasks with real-time citations, while ChatGPT excelled in procedural queries. Students’ feedback emphasized clear, task-specific prompts, highlighting prompt literacy’s role in effective AI-assisted learning. These findings advocate ethical prompt design to enhance human-AI interaction in education.

Queer online communities across social media platforms serve as critical spaces for LGBTQ individuals to seek and share information. However, few validated tools exist to assess LGBTQ users’ experiences in these communities. As part of a dissertation study, this poster presents a cross-platform, LGBTQ-centered multi-scale assessment instrument measuring five key dimensions: ease of use, usefulness, sociability, satisfaction, and continuance intention. Each scale was adapted from existing validated measures and contextualized for information seeking and sharing across popular platforms used by LGBTQ people, including Reddit, Discord, Instagram, and TikTok. Data were collected through an online survey of 92 LGBTQ university students from 44 U.S. higher education institutions. Internal consistency was assessed using Cronbach’s alpha and item-total correlations, with results indicating acceptable to strong reliability. The preliminary findings support the instrument’s potential for future use in user experience evaluation, inclusive platform design, and LGBTQ information behavior research. Confirmatory factor analysis and construct validity testing are planned for future studies with larger and more diverse samples.

Serving immigrants has been an essential part of library services since the early 20th century in the United States. However, despite the long history, the repertoire of immigrant services in libraries has not evolved much beyond the traditional set of language-learning programs, often criticized due to the perceived low relevance of the resources and lack of accessibility. To address such a disconnect between intended users and provided services, participatory methodologies have been encouraged when working with historically and traditionally marginalized communities. Zooming in on the precise intersection of U.S. library workers’ experiences of employing participatory practices in their work with local immigrant communities, this mixed-methods study aims to create an overview of the library-immigrant community participatory network that shows how distinct actors are related and influence the process as well as the outcome of co-design efforts from the library workers’ perspectives.

This study explores the perceptions of librarians involved in collection development. Furthermore, the prevalence and impact of biases—both implicit and explicit—on the collection development process in public libraries. Data were collected through in-depth interviews. A total of 13 participants were interviewed based on a self-selection sampling strategy. Findings reveal that librarians define social justice in collection development based on their lens. However, they acknowledged a unanimous agreement on the presence of implicit biases in collection development. The study found that 50% of participants had received some form of bias-related training, either through formal education, mandatory programs, or self-directed learning methods such as reading, videos, and conference sessions. Notably, those with more comprehensive training or greater familiarity with bias concepts demonstrated a clearer understanding and more positive attitudes toward strategies for mitigating biases.

This preliminary research is the first step in investigating how different implicit demographics and dialects influence ChatGPT responses. We crafted eight different prompts - all language or dialect 'translations' of "Tell me a story about a queen who kills her husband and marries his brother" - and collected six ChatGPT responses to each prompt. We are in the process of investigating two questions: 1) How does ChatGPT change (or not) responses based on implicit demographic or dialect indicators contained in user-created prompts?; and 2) What do those responses reveal about the biases and assumptions present in ChatGPT’s training model? We have completed a single phase of analysis, and found that the responses are distinctly European in style and tone, that the Southern USA dialect prompt is poorly interpreted by ChatGPT, and that prompts in other languages or written by English Language Learners result automatically in shorter stories with simpler language.

A Pew research study reports that after the COVID pandemic, around 37% of U.S. teens have mental health issues and other reports that 48% of teens attribute negative effects to social media. Initial explorations among emerging adults have shown that even though social media apps are still the primary choice of information source, the use of generative AI is on the rise. However, it is not yet clear how emerging adults between the ages of 18 and 24 evaluate information and determine credibility and trustworthiness of health information sources that are based on artificial intelligence (AI). This project is a work in progress and intends to explore the information behavior and practices of emerging adults in New Jersey who use generative AI for health information. Using data obtained from observation-based interviews of selected participants, selective coding will be used to examine how the concept of cognitive authority can help to inform information behavior and practices of emerging adults. This research is conducted through a cognitive authority (CA) lens with the hope that an understanding of the relationships between emerging adults and health information sources may provide insights into better strategies to provide access to trustworthy and credible health information.

The widespread use of AI chatbots has incited concerns about how chatbots will impact university students. Although research has examined several aspects of how students interact with chatbots in relation to their academic work, there lacks a baseline understanding of how they use chatbots as an information resource in their day-to-day lives. In this poster, we present findings from an exploratory survey of 199 college students on their use and assessment of chatbots as an information resource for a variety of domains. Our findings reveal that college students make few distinctions between chatbots and search engines as information resources for most topics. We discuss the implications of our findings for future education and research directions.

Deepfake technology presents serious risks, especially for seniors vulnerable to misinformation. Educational interventions are thus required. This paper reports a participatory design workshop where seniors created low-fidelity educational game prototypes simulating real-world deepfake scenarios, highlighting key design principles such as real-world contexts and interactive feedback. The findings demonstrate the helpfulness of using participatory design to create educational games for seniors.

Black trans men navigate digital spaces that simultaneously marginalize them and serve as vital sources of information, community, and identity formation. While existing research explores the online information practices of marginalized groups, limited scholarship specifically examines how Black trans men use digital platforms to construct identity, access resources, and build community.

This study investigates their digital information practices within sociotechnical systems that structure visibility, participation, and belonging. It addresses three core research questions: (1) What are the digital information practices of Black trans men? (2) What types of digital communities and technologies serve as information resources? (3) How do online spaces influence identity formation, visibility, and engagement? The research uses Critical Technocultural Discourse Analysis (CTDA) and semi-structured interviews to examine how Black trans men curate, share, and interpret information online. This research also uses a theoretical framework shaped by Black ontology, epistemic resistance, and intersectionality to understand the role of power and exclusion in digital information practices.

Overall, this work analyzes systemic power dynamics in knowledge production and digital exclusion through an examination of digital platforms as locations for both constraint and resistance to contribute to discussions about race, gender, and information marginalization.

In China, the influence of large language models (LLMs) in text creation has been growing rapidly. However, some of which may not reach users’ expectations. Taking China’s publishing industry as an entry point, this study systematically explored the practical application of LLMs. Four China’s well-known LLMs were selected. The dataset was constructed by collecting the advertising texts of the top 100 books on the 2024 sales ranking of the Top1 e-commerce book sales platform “Dangdang.com” (like Amazon Books in America), followed by data annotation by two publishing major postgraduate. The study reveals that current China’s LLMs have shortcomings in capturing the core selling points of books and identifying users’ pain points. Meanwhile, they also exhibit inaccuracies in text emotion analysis and linguistic style discrimination. To address these issues, this study proposes optimization strategies that incorporates the “Pain Point–Selling Point–Solution–Evidence” logic framework into prompt engineering, along with fine-tuning the model with emotional vocabulary data, to enhance the model’s text creation effectiveness in the publishing field.

Informed consent is considered a fundamental ethical and legal principle in research, with requirements determined at the national level, resulting in differentiated levels of compliance across countries. With such requirements, what are the implications for Indigenous communities, where collective consent and relational ethics matter most (Smith, 2012)? This poster explores refusal as a generative Indigenous methodological and ethical stance, grounded in sovereignty, care, and accountability (Tuck & Yang, 2014). Through a comparative case study of Amos Bad Heart Bull’s ledger drawings and the isiXhosa initiation ceremony of ulwaluko, we argue that refusal is not silence, but an active form of cultural governance that determines what knowledge may be shared, by whom, and under what conditions (Simpson, 2007). Drawing on the work of Simpson (2007), Smith (2012), Tuck and Yang (2014), Gasa (2007), and Hlabangane (2021), this poster interrogates authorship, feminist positionality, and the role of land as ceremonial actor. It advances a cross-cultural framework for honoring refusal as a sovereign act of care and knowledge protection. This poster invites the audience to disrupt their notion of informed consent and to engage in the difficult conversation of refusal in library and information science research.

Public libraries are important support for refugees, but how they respond to trauma and special needs remains underexplored. This study examined refugee services in U.S. public libraries and combined the perspectives of librarians and refugees. Data was collected through a national survey of 229 library staff, five focus groups with librarians, and five focus groups with refugees to discuss the potential of trauma-informed care (TIC) to improve library services for refugees. Results showed that many public libraries serve refugees, partner with social workers, community organizations, and governments, and have begun to consider refugees’ trauma and integrate TIC by training staff and creating a welcoming environment. Future research will focus on detailed data analysis and customized TIC training.

This study examines how academic structure influences research focus in Library and Information Science (LIS) schools. We analyzed 16,671 publications from 745 faculty across 42 U.S. programs. Schools with independent LIS units demonstrate diverse research portfolios, while those sharing administration show variations: computer science affiliations emphasize technical topics with minimal traditional library research, education affiliations focus on library science and metadata, and communication affiliations balance traditional areas with social media research.

The rapid advancement and adoption of generative artificial intelligence tools in academia have raised critical questions regarding policy readiness and ethical governance, especially in Higher Education Institutions (HEIs) across the Global South. This study investigates the existence of institutional AI policies in three Sub-Saharan African HEIs and explores their implications for academic activities. Using the Times Higher Education rankings, we selected the top ten HEIs in Nigeria, Ghana, and South Africa each. We first used structured website content analysis across institutional portals, graduate school portals, and library webpages to identify published AI policies. This digital audit was supplemented by follow-up information interviews with key university personnel to verify the existence or absence of such policies. Guided by our preliminary research questions, preliminary findings show that while a few HEIs in South Africa have published and implemented institutional AI policies, no HEI in Ghana or Nigeria currently has a formal or publicly accessible AI policy in place. In these countries, academic staff use their discretion to make subjective judgments when evaluating student submissions suspected to be AI-generated. This creates inconsistencies, which in practice raises concerns about fairness, transparency, and accountability.

Artificial intelligence (AI) promises to streamline workplace information practices, yet little is known about adoption in public-sector contexts. We surveyed 103 Israeli public-sector knowledge workers on (a) their willingness to employ AI for 16 work-related information activities and (b) their actual use of such tools. Principal-component analysis grouped the activities into three categories: strategic & analytical tasks, management tasks, and personal applications. Across all categories, paired-t tests showed a large willingness–use gap (e.g., strategic tasks: M = 3.67 vs. 1.88, p < .001). Multivariate regression revealed that AI availability strongly predicted use (η² ≈ .14–.22) but weakly predicted willingness (η² ≈ .04–.05). Results suggest that providing access is necessary but not sufficient; motivational and organizational factors moderate adoption. We outline design and policy steps—trust-building, role-aligned training, and low-risk trial contexts—to bridge the gap and advance human-centered AI in the public sector

This study examines the application of agentic AI to evaluate the effectiveness, accuracy, and efficiency of academic libraries' responses to student inquiries. Data were collected by analyzing frequently asked questions (FAQs) from the University of Wisconsin-Milwaukee library website using OpenAI’s large language models (LLMs), o4-mini. The analysis focuses on identifying the types of questions that are most appropriate for AI-assisted responses and how agentic AI can support librarians in delivering targeted and timely assistance. The findings reveal that agentic AI can significantly improve the quality of library services by providing more timely and targeted student support. This research seeks to improve the effectiveness of librarian responses, strengthen librarian-student interactions, and support the meaningful integration of agentic AI into academic libraries.

Digital tools, including notation software, digital audio workstations, and other technologies have become ubiquitous in the process of music composition. Composers likewise use digital means to document their lives and careers. Musical scholarship and the cultural record relies upon continued documentation. Yet, obsolescence of hardware and software, the fragility and variety of digital file formats, media prone to degradation, and poor metadata place this material and subsequent scholarship at risk. This poster presents survey results of composers regarding their process, workflows, and knowledge of preservation practices. Our findings illustrate the challenges faced by music creators, and will inform the creation of a toolkit to bridge the gap between composers’ personal preservation practices and their archival partners’ ability to provide access to the historical record.

This study investigates users’ identification of virtual influencers in social media. A mixed-methods study consisting of an eye-tracking experiment and an interview with 60 participants was conducted. The results show that users can identify VIs with moderate accuracy. Users have relatively comprehensive but insufficient identification criteria and complex attitudes toward VIs. These findings suggest implications for AI and media literacy education.

Drawing on the Uncanny Valley theory, we conducted an online experiment to examine how avatars with varying human-likeness (i.e., real human, deepfake, human-like, and cartoon) affect homophily, message credibility, and concept understanding in anti-scam public service announcements (PSAs). Results showed that the human-like avatar was perceived as less homophilous but more credible. Additionally, the deepfake condition closely mirrored the outcomes of the real human condition, suggesting it may cross the Uncanny Valley. This research offers timely insights for the deployment of AI-generated avatars, particularly in guiding how to calibrate avatar realism.

Through this inquiry, we identify associations between immigration-related terms in the library and information science (LIS) literature over the last decade. We report on an exploratory data analysis of selected keywords in immigration-related LIS scholarship, interpreting legal–criminalized and individual–social and societal distinctions. Our preliminary findings suggest that associations between migration-related keywords are essential for LIS scholars to reflect upon, and we emphasize the responsibility of wielding keywords with attention to the connotations they may reinforce or reimagine.

Textual analysis remains challenging for researchers without programming expertise, creating barriers in social sciences and humanities research. This poster presents XXXX Tool 2.0, an enhanced web-based application that democratizes sophisticated textual analysis through natural language processing technologies. Building upon version 1.0, XXXX Tool 2.0 significantly expands data source integration to include HathiTrust Digital Library, Dimensions AI Database, and PubMed, alongside existing Scopus, Web of Science, Lens, and customized file support. Enhanced analytical capabilities include established methods for topic modeling, network text analysis, keyword stemming, in addition to new features such as burst detection, scattertext visualization, and sentiment analysis.

Bringing museum information resources online benefits the public and researchers. Despite decades of digitization, open access, and collaborative data sharing initiatives, few empirical studies have evaluated information retrieval effectiveness and open data quality. This poster uses web-based content analysis, descriptive statistics, and a case study to review over 180,000 Chinese antiquities in 61 U.S. museums. Findings reveal intertwined curatorial and cataloging practices, challenges around the structural-flexibility tradeoff, and limited inter-institutional data interoperability. This poster contributes to museum informatics, digital scholarships, and provenance research.

This study investigates professional designers' integration of AI tools within workflows across five key stages: Discovery & Framing, Ideation & Exploration, Development & Structuring, Refinement & Decision-making, and Finalization & Delivery. Semi-structured interviews with 14 designers reveal varied AI engagement, shaped by creative intent, identity, and AI trust. A heatmap illustrates AI supporting efficiency and creative tasks (ideation, content generation, refinement); interpretive tasks requiring contextual judgment are human-controlled. AI is viewed as an assistive co-creator accelerating iteration and automating labor, not replacing core creativity. Offloading procedural tasks and early ideation to AI enables deeper focus on conceptual and critical decisions. Designers use creative agency to reconfigure workflows, fostering reflective, conceptual work. Findings suggest further exploration of human-AI interaction, on how designers balance human-centered values and creative performance, maintaining agency and ethics.

This study initiated the development of a new scale to measure perceptions of information credibility on the web, distinguishing between trustworthiness and expertise as core dimensions. Building on a previous analysis of the literature on credibility assessments of information on social media, we generated a pool of 15 items, six for trustworthiness and nine for expertise. Following established scale development guidelines, we refined the scale through expert reviews and cognitive interviews with users. Experts (n = 2) rated most items as highly relevant and offered suggestions for refinement, such as including new items. Users (n = 9) confirmed item clarity but raised concerns about certain terms and perceived objectivity. These preliminary findings support the scale’s relevance and will inform revisions for subsequent validation. Ongoing work will expand participant feedback and move toward large-scale empirical testing.

This study investigates the viability of artificial intelligence (AI) supplementation in librarianship, focusing on its potential to enhance, rather than replace, reference and information services in public libraries. With the growing integration of AI technologies in various professional fields, including information science, this study seeks to explore how AI can be ethically and effectively implemented to support librarians while preserving the profession's core values of personalized service, intellectual freedom, and privacy. The study employs a quantitative research design, utilizing an online survey distributed to public library staff across the state of Connecticut, United States of America. A total of 62 usable responses were received. Findings indicate a complex landscape of attitudes toward AI integration. While many respondents acknowledged AI's potential to streamline administrative tasks, improve reference efficiency, and assist in cataloging and data management, significant concerns were also raised. Key apprehensions include data privacy, job displacement, algorithmic bias, and the potential erosion of critical thinking skills and personalized patron service. Participants frequently stressed the importance of maintaining human oversight and ensuring equitable access to AI technologies. This research highlights the necessity of balanced implementation strategies that emphasize transparency, staff training, and clear ethical guidelines.

Modern virtual reality (VR) and augmented reality (AR) tools possess advanced capabilities that are reshaping archaeological education for public audiences in venues such as museums and schools. Research is needed to determine how these applications modify public perception and interaction with history and cultural heritage. This paper examines the perceptions of academic archaeologists regarding the integration of VR tools into archaeology education. We conducted semi-structured interviews with six archaeologists who either teach archaeology in a university or museum setting, or who work with archaeological materials in the field. We identified the major concerns and perceived benefits of VR integration in these environments. Our work contributes an important baseline assessment of attitudes and practices within the field so that we may better understand the existing gaps among archaeologists and historians, as well as the extent of those gaps.

As the use of machine learning expands across domains, the importance of data documentation practices and corresponding frameworks has grown to facilitate transparency. While the development of metadata frameworks is one of the core concerns in the information science field, little is known about how information science professionals contribute to data documentation framework in ML contexts. Therefore, this study explores information science professionals’ current engagement in data documentation framework development for ML studies. We conducted a literature review in a systematic way with the assistance of a generative AI tool, followed by identification of authors’ professions. Our preliminary findings show that information science professionals have play limited leadership roles in developing data documentation frameworks and tend to serve supporting roles instead. We conclude by acknowledging limitations and identifying areas for further analysis.

National programs for educational technologies often emphasize online learning, built on claims of universal access and improvement. However, these narratives frequently overlook the complex educational contexts of marginalized rural communities, such as those in Nigeria. This paper reframes rural educators in Nigeria as information professionals who engage in community-specific knowledge dissemination, advocacy, and literacy development. Drawing on critical rural theory, decolonial pedagogy, and information behavior models, this conceptual paper argues that rural educators enact the very functions ascribed to information professionals. As a work-in-progress paper, it calls for contextually relevant educational practices and outlines future directions for empirical study, policy, and LIS curriculum development.

Public library staff often express feeling unprepared to engage with people experiencing crises that require des-escalation. This poster details the iterative development and refinement of a Generative AI (GenAI) chatbot designed to enhance immersive virtual reality (VR) training simulations for public library staff. Building on previous work exploring VR for de-escalation training, we trace the evolution of our simulated patron—from "Michael 1," a live-roleplayed avatar in Mozilla Hubs, to "Michael 2," an initial GenAI chatbot integrated within the Engage platform, and culminating in "Arthur," a more sophisticated GenAI chatbot being developed in a custom Unity environment using Python, C#, Blender, and various APIs (TTS, STT, LLM). This project focuses on leveraging GenAI to create realistic, interactive practice scenarios, aiming to improve staff confidence, skill development, and ultimately foster more effective, empathetic crisis response in public library settings.

Research on the benefits of students’ use of generative Artificial Intelligence (AI) has been notoriously mixed: some findings suggest AI use can enhance learning and development while others suggest it can be harmful. Adding to this lack of clarity is the fact that there is little available literature regarding the prevalence of AI use among students, the student factors that contribute to AI use, and the downstream effects of students’ AI usage – factors likely important for understanding the roles AI use might play in educational outcomes. Institutions report observing rapid increases in AI use among students, however associated research has not kept pace with rapid advances in AI capabilities, accessibility, and potential uses. In addition to clarifying the impacts of AI on students, institutions need this information to inform policies and effective practices regarding use of AI in education. This study summarizes the available data on generative AI usage from the 2023–2024 Healthy Minds Survey (HMS), a cross-sectional study of United States of America (USA) college students, to address this knowledge gap.

As in-vitro fertilization (IVF) becomes increasingly accessible, patients are turning to social media to supplement clinical information and emotional support. This study investigates the fertility-related content shared on Facebook by IVF healthcare providers, focusing on Traditional Chinese-language posts from both individual physicians and organizational accounts. Using keyword-based searches, we identified 40 public accounts and analyzed 4,285 posts published in 2024. Posts were categorized by month, content theme, media format, and interactivity. Findings revealed clear differences in communication strategies. Personal accounts emphasized outpatient information, educational explanations, and service promotion, with peak posting activity in the fourth quarter. Organizational accounts focused on case sharing and internal work documentation, maintaining a steadier posting rhythm throughout the year. Personal accounts predominantly used image-and-text formats, while organizational accounts more frequently employed short videos to enhance engagement. This study highlights how IVF-related health communication varies across account types, offering insight into how content is framed and delivered in Taiwan’s digital health landscape. The findings inform future strategies for more audience-specific and platform-appropriate reproductive health outreach.

The goal of eradicating friction has driven the development of information spaces. Yet, the problems associated with a loss of friction have led many to suggest ways to reintroduce friction. Unfortunately, libraries are often overlooked in these discussions. This poster outlines an initial framework for the role of librarianship in introducing necessary friction in ways that improve the quality of society’s navigation of information spaces.

To address the need to study the role of social media during political crisis in non-Western context, this study investigates Facebook digital activism during the peak of the July 2024 student-led revolution in Bangladesh. Through thematic and lexical analyses of 4,000 comments from 60 protest-related posts, we found that most comments (57.8%) in our sample were ideological, unprovocative, and hashtag-driven. Provocative comments (24.6%) exhibited higher vocabulary density and rhetorical variation than unprovocative comments. While some comments supported the protest, others attempted to delegitimize it. By focusing on this political non-Western crisis, this study challenges the binary view that provocative trolling and digital activism are mutually exclusive and instead it underscores their overlapping roles in online political discourse.

This study investigates data risks in human-AI interaction within personal health management apps and systems. Drawing on literature, cases, and policies, it analyses data types, governance practices, and potential risks. Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and thematic analysis, 116 articles, 9 cases, and 4 key policies were examined. Identified data types include physiological indicators, lifestyle habits, personal identity, and social relations. Governance practices and risks are categorised into six dimensions: governance subjects, policies and standards, technologies and facilities, lifecycle measures, data rights protection, and risk management. The study offers governance strategies for human-centred AI and lays the groundwork for further assessment of risk types and severity.

We introduce AnnoLoom, a tool designed to assist researchers with codebook development, annotation tasks, and evaluation of human vs. AI’s annotation results. AnnoLoom contributes to human expert-AI collaboration and its efficacy in the context of using Large Language Models (LLMs) for research involving text-based data. We conducted a cognitive walkthrough to iteratively improve the design of AnnoLoom. We discuss the design of the tool and future work in evaluation.

Social facilitation—formal and informal training and assistance in using information systems—is crucial for enabling people to access, use, and benefit from ICTs. To reduce digital inequalities, it is important to understand how marginalized communities overcome barriers and sustain effective ICT use. Based on ethnographic fieldwork with a campesino organization on the border of the Colombian Amazon, this research examines the forms of social facilitation that support meaningful ICT engagement in a community served by remote infrastructures. Findings show that formal social facilitation in this setting mirrors the unreliability of physical infrastructure, offering support through intermittent interventions by state agencies, NGOs, and researchers. Community members employ four key practices to derive meaningful knowledge from these sporadic interventions: repeated training as habituation, note-taking, tinkering, and extending incursions online. Between these external efforts, the community collectively addresses ICT challenges, forming emergent local spaces of ICT support that enable situated and ongoing transitions to the information age. This research sheds light on the everyday digital practices of communities often excluded from dominant narratives of the digital society. It also expands the concept of social support beyond formal venues such as libraries, community technology centers, and cybercafés.

Query reformulation is a critical aspect of the exploratory search model. During exploratory browsing, searchers issue broad queries to learn about the breadth of the topic; during focused searching, they issue narrow queries to learn about a specific aspect of the topic. Typically, little support is provided by the search interface to help searchers refine and reformulate their queries. This research addresses this gap, demonstrating a generative AI approach to suggesting both broad and narrow queries. Using a timeline of past search activities (queries issued and search results saved), the approach allows searchers to reformulate their queries based on specific saved search results, previous queries issued, or the current query. This gives the searcher interactive control not only over whether to request broad or narrow query suggestions, but also upon what information to base those suggestions.

The relationship between data governance and data organization is cyclical and mutually reinforcing. Data governance defines the frameworks, standards, and roles that shape how data should be organized, accessed, and used. These guidelines ensure that data is secure, consistent, and aligned with institutional goals. At the same time, the structure and management of data – the core of data organization – significantly influences governance. As institutions evolve, changes in how data is stored, shared, and analyzed often necessitate updates to governance policies. Well-aligned data governance not only standardizes data definitions and security measures but also breaks down silos and encourages cross-unit collaboration for the common institutional strategic goals. This dynamic interplay is particularly important in complex, decentralized environments like higher education. Adopting Anthony Giddens’s Structuration Theory (Gidden, 1984) and Orlikowski’s Structurational Model of Technology (Orlikowski, 1992, 2007), this study proposes a new theoretical framework – Structuration Data Governance Model – to analyze the interaction between data governance and data organization. It also suggests practical applications of the theory.

Online discourse about contentious and ideological issues like undocumented immigration, trigger massive user reactions. Online hostility towards marginalized groups and its effect on them have attracted scholarly attention. However, reactions to posts on the same issue may take different sides; they may vary not only because of the specific views expressed, but also because of the perceived social roles of those who post about the issue. We examined how users’ provocative comments vary among posts by the public, politicians, and immigrants. Given this controversial topic, we found that almost 85% of the 4,800 comments were provocative. We found that the prevalence and nature of these comments varied significantly between posts by the public and those by either by politicians or by immigrants; We also found significant variations between posts on Facebook and on X.

Artificial intelligence (AI) now appears on the search page and this may influence how users interact with search results. This study examined how this development in the information seeking process affects people’s Health Information Literacy (HIL). Using a HIL measurement for literacy indicators, young adults were presented with a simulated Google search page. Findings show that participants disliked Google’s design for this task. Frequency coding revealed that only 17 of the 41 participants used AI to improve their health insurance literacy. Participants revealed that they felt AI should provide a simplified explanation of health insurance information.

Qualitative data analysis (QDA) is a process of knowledge construction through human interpretation. Designs for computational tools supporting the QDA have abounded for decades, and most recently, have focused on how to infuse Artificial Intelligence (AI) supportive features. Yet, despite communities of qualitative researchers’ routine engagement in sensemaking, frameworks portraying their moment-by-moment engagement with data remain largely underexplored in the qualitative methodological literature. This gap constrains designs of human-centered, epistemologically aligned AI-infused Computer-Assisted Qualitative Data Analysis Software (AI-CAQDAS). Unlike traditional CAQDAS, which focused more on supporting data organization and retrieval, AI-CAQDAS is designed to assist with qualitative sensemaking and moment-by-moment interpretation of data. This proposed study investigates how qualitative researchers construct and shift their epistemic relationships with data during analysis, foregrounding the role of researcher positionality, including disciplinary background, social identity, and methodological commitments, in these interactions. Using a think-aloud protocol on QDA tasks with participants who are experienced qualitative researchers from diverse disciplines, we apply positioning theory to make visible the often-invisible processes of qualitative data sensemaking to challenge the prevailing code-and-retrieve logic embedded in current AI-CAQDAS designs. We argue for an urgent need to design AI-CAQDAS that supports nuanced human conversations at the heart of qualitative sensemaking.

AI bots, deepfakes, and the misinformation they generate on social media prompt users’ behavioral and affective reactions. While the relationships between social media use and emotion are frequently studied, less is known about how users' responses (e.g., fact-check, report posts/posters) are related to affective outcomes. This survey of undergraduates using X (known for AI bots and misinformation) found that Disappointed, Disgusted, and Angry were the top emotions after users responded to issues on social media. A latent class analysis identified four user profiles. The most prevalent class shows positive emotional outcomes, but mixed with anxious feelings. The latter was notable across multiple profiles, suggesting an area for intervention.

This paper analyzes 25 health information behavior studies published in the period of 15 years (between 2009 and 2023) using eye-tracking as the research methodology. Eye-tracking technology has become a valuable tool to study information behavior. This study examines the ways of adopting eye trackers in health information behavior research and how the research is designed with an extended method the technology provides. This paper contributes to understanding methodological trends for health information behavior research.

This pilot study applies intersectionality and insider/outsider dynamics to examine mental health information-seeking practices among Chinese LGBTQIA+ immigrants in the US. Through semi-structured interviews and thematic analysis, the findings reveal that intersecting identities influence participants’ perceptions of insiders and outsiders when seeking mental health information. Participants further categorized insiders into social types based on behaviors, authority, and intimacy. Information and communication technologies (ICTs) facilitate interaction among trusted insiders across geographic distances. This study advocates for the design of culturally competent mental health information resources that reflect intersecting identities and insider trust dynamics.

Artificial intelligence has undergone an iterative evolution from ‘reactive’ to ‘discriminative’ to ‘generative,’ with its penetration into society becoming increasingly widespread. As a result, the cultivation of children's artificial intelligence literacy has become increasingly important. China's unique technological ecosystem, cultural context, and government priorities provide a favorable backdrop for research. However, we know little about the mechanisms and content of AI literacy development among Chinese children. In this exploratory study, we conducted interviews with children from different regions of China through both online and offline methods. Through coding analysis of the interview records, we found that Chinese children develop a series of skills through a bidirectional interaction between ‘cognition and behaviour.’ After independently mastering these skills, children internalize AI literacy. Based on this, Chinese children's AI literacy can be categorized into cognitive-associated literacy, behavioral-associated literacy, and skill-associated literacy. These findings provide insights for establishing a child-centered framework for AI literacy.

This work-in-progress study examines how researchers in medical and health sciences engage in data sharing, focusing on Data Availability Statements (DAS) from articles published in three mega journals indexed in PubMed Central. Preliminary results reveal a variety of DAS categories, each reflecting a distinct approach to sharing research data. The distribution of these types was broadly similar between articles with and without Korean-affiliated authors. This study could contribute to understanding how data sharing practices are evolving in medical and health research and highlights opportunities to better align global norms with national-level practices.

Over the course of 2020, as the COVID-19 pandemic was unfolding across the world, numerous information institutions implemented COVID-19 rapid response collections. Rapid response collections are an emerging collecting modality that focus on documenting an event as it happens or in the immediate aftermath. Growing literature reflecting on these collections typically tells the story of individual collections and focuses on the process of starting and building them. Five years after these collections initially began, there is now the opportunity to reflect on the scope and impact of rapid response collecting during the COVID-19 pandemic, as well as the status of these collections today. This pilot study, which aims to provide a foundation for a broader understanding of rapid response collecting across US GLAM institutions, assesses the presence, scope, and status of rapid response COVID-19 projects started at universities in Pennsylvania.

Palliative care is described as health service to patients and caregivers to improve quality of life when facing a life-threatening illness. We analyzed preliminary 46 TikToks tagged with #palliativecare (mostly uploaded between 2024 and 2025) to enable first insights into who as creator and what content stand behind the #palliativecare (RQ1). To this end, we developed a codebook (inductive and deductive approach). Almost half of the TikToks were posted by healthcare professionals, while 30% were created by patients. The content of the TikTok varies, while most of them are categorized as a Personal Experience (61%). Furthermore, we aimed to gain first insights by analyzing 10 comments on five randomly selected TikToks shared by different types of creators. We wanted to explore research potential surrounding affective information behavior and the emergence of parasocial relationships in response to highly vulnerable and/or emotionally charged content (RQ2). TikTok users provide emotional support by showing sympathy, prayer and are also asking questions. This work-in-progress study enables first insights into relatable and personal content considering palliative care. These insights could be used to improve health communication and deepen our understanding of belonging within these vulnerable, emotionally charged communities.

Data governance in practice is shaped by a complex interplay of socio-legal frameworks, institutional arrangements, cultural attitudes, and technological capabilities. Understanding these interlinked dimensions is essential for effective management of information systems, particularly in sectors that are highly data-dependent for forecasting and delivering social services, like transportation. This study explores these dynamics through the specific case of vehicle speed data governance. Employing a mixed-methods approach, the research combines computational topic modeling using Latent Dirichlet Allocation (LDA) on a corpus of 237 policy and institutional documents with qualitative analysis to identify key governance themes. Findings reveal the prominence of national reporting mandates and technological adoption at the local level, alongside notable gaps in data lifecycle management and stewardship. To complement these insights, forthcoming interviews with data managers aim to clarify governance processes and challenges not evident in policy texts. This work advances our understanding of how socio-technical and legal factors collectively influence the governance and effectiveness of urban data infrastructures.

Game-based learning represents a promising avenue for educators to increase student engagement and academic results. However, research has found mixed results in the effectiveness of games to improve various learning outcomes. Researchers view artificial intelligence (AI), particularly large language models (LLMs), as augmenting technology to increase the interactivity of games, yet it’s unclear how effective integrating generative AI within games is for improving student learning. This study seeks to identify how researchers in various disciplines have incorporated AI into educational games through a preliminary systematic review using the PRISMA framework. After analyzing 12 empirical studies, we found that researchers largely compared generative AI games against traditional teaching methods through experimental studies. Moreover, these studies largely reported positive results for the LLM-integrated games in increasing engagement, particularly in STEM and AI literacy domains. However, further research should be conducted to understand the effectiveness of LLM-infused games in improving long-term learning outcomes.

This poster details the findings from a study investigating the efficacy of community based and organized information sessions around New York Police Department smart city surveillance practices in New York City. The purpose of this research is to better understand localized perspectives on surveillance infrastructures within New York City and promote community dialogue through local public libraries and community groups as a mode of information activism.

Doctoral students face a lack of information early in their programs, creating an information need that may be addressed through peer interaction. While prior studies have looked at this interaction in structured environments, much less is known about unstructured ones. This paper reports on a study that explored peer interaction and information seeking and sharing of doctoral students in unstructured environments. Based on twenty semi-structured interviews with social sciences and humanities students, it was found that information on academic, administrative, personal, social, and tips and tricks is exchanged among peers. The study underscores the importance of having access to unstructured environments (e.g., shared workspace) to satisfy the information needs of doctoral students.

When chatbots lack physical bodies, how relatable should their body advice be? Concerningly, when an AI chatbot generates health information, it is unaware of its audience and contextual sensitivities. This study explores whether AI’s neutral tone compensates for its lack of framing in this context. It examines which contextual sensitivities young women preferred from Google’s AI chatbot (Gemini) about diet advice and why it matters. Findings suggest that young women feel AI lacks contextual awareness when framing sensitive diet issues. Specifically, participants feel the AI prioritizes a “neutral” or “technical” tone when they prefer a reassuring framing of the information.

Consumer health informatics is becoming increasingly important in a world where more than 5 billion people rely on the internet and social media for health information. eHealthyinfo is a physician-initiated and reviewed artificial intelligence (AI) applied digital health information website, aiming to be essential in advancing health literacy and promoting preventive health behaviors. To assess the effectiveness of its digital content, we conducted a mixed-methods evaluation of 13 wellness and nutrition videos randomly selected from eHealthyinfo, representing 30% of its relevant content. Two independent reviewers used the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) and the modified version of the CDC Clear Communication Index (CCI) to evaluate each video. Website log analytics were also collected to assess user engagement. The average PEMAT scores were 89.5% for understandability and 100% for actionability, while the average CCI score was 94.4%, indicating understandable, actionable, and clear content. eHealthyinfo demonstrates a strong potential as a digital health education tool, with high-quality, user-friendly content.

The widespread use of generative AI (GenAI) is seen as beneficial for team collaboration, yet full access for all members is often impractical in real-world settings. This study explores how different GenAI integration structures—no access, unequal access, and full access—affect team performance, focusing on the mediating roles of team communication style differences and communication effectiveness. Findings from a lab experiment involving 60 two-person teams reveal that teams with unequal GenAI access demonstrate higher communication style differences and superior performance compared to teams with no AI or full access to AI. Two mediators—team communication style differences and communication effectiveness—sequentially mediate the impact of unequal AI access on enhanced team performance. This research provides theoretical insights into the effects of varied GenAI integration structures on human-AI collaboration and offers practical guidance for optimizing team design in human-agent systems.

This study investigates data literacy and its impact on information anxiety through the mediation of cognitive overload. We surveyed students, faculty/staff, and community library users at University of Rochester (UR) Libraries during the Data Bloom 2024 and Love Data Month 2025 events. Over 100 participants self-reported their information anxiety pre- and post-training using Likert scales. Partial Least Squares Equation Modeling showed significant association between data literacy, cognitive overload, and information anxiety, with cognitive overload serving as a strong mediator. Multigroup Analysis further revealed that the effect of data literacy on post-training anxiety varied across participant roles and disciplines, with students and health/social professionals benefiting more than STEM participants and faculty/staff. The findings highlight the value of academic libraries in promoting data literacy and reducing anxiety around data use.

This poster reports preliminary analysis of an online survey of 191 college students in South Korea examining their use of Generative Artificial Intelligence (Gen AI) tools, perceived benefits, and institutional supports. Participants mainly used Gen AI for various purposes in academic/work-related contexts, such as Assignments/Writing Reports, where most participants perceived the benefits. However, participants also used Gen AI in everyday life/non-work contexts, notably for areas such as travel planning, entertainment recommendation information, and mental health improvement methods. Only 33.5% of participants received support for using Gen AI from universities/university libraries, which suggests opportunities to help students learn how to use Gen AI effectively and responsibly. This research contributes an empirical study of user behaviors around Gen AI use in rapidly changing sociotechnical environments, for academic/work-related and everyday life/non-work contexts, calling for more institutional support and human-centered research for equitable access to and effective use of Gen AI for college students.

This analysis uses Derridean deconstruction and reconstruction to examine the relationship between privacy, the at-home experience, and hospitality from the perspective of information ethics. It is argued that informational privacy is foundational in ensuring the informational at-home, establishing one as host rather than hostage in the informational spaces required for the development of being and knowing. The informational at-home, in turn, informs the possibility of hospitality. Hospitality is deconstructed beyond industry or transaction, instead revealing an etymological and conceptual depth that grounds the possibility of information ethics.

Unhoused patrons are uniquely vulnerable to marginalizing public library policies. To first capture the scope of the problem and then to offer a lens for productive critique, I perform a content analysis on the behavior policies of all thirty-five library systems in Los Angeles County, California, USA, describing the methods by which libraries deny unhoused patrons access to library spaces. Then, by applying Iris Marion Young’s work on “enabling justice” to these policies, I give a name to these processes: oppression. I argue that if public libraries are to live up to their name, policies that affect public access need to be viewed through a justice lens. Public libraries that truly are “public” need to design policies that enable justice for all members of their public, and the methods I apply to the Los Angeles County case study offer tools to aid that transformation.

Social movements have been using social media to share their narrative which supports their goals, values, and grievances, and it usually spreads across platforms in visual form. This research creates a framework to specifically look at how social movement images are structured into a narrative based on their visual semantic relationships. In the framework, the images go through feature embedding, and zero-shot captioning to create a network of semantic relationships. These relationships are represented in an ontological network where the labeled images are nodes and their descriptions are the edges semantically connecting them based on similarity scores. This network is analyzed using community detection models to understand the latent visual semantics supporting social movement images. Comparing the feature labels and visual semantic image captions graph to a graph of semantically similar images proved to be somewhat difficult for domain-specific images like social movements. The combined framework analysis shows the various nuanced themes that arrive even within grouped classes.

The rapid evolution of artificial intelligence (AI) continues to reshape how information is produced, shared, and accessed, raising urgent questions about equity, accountability, and fairness. This poster, inspired by the upcoming volume Assessing AI and Social Justice Intersections: Challenges and Opportunities, explores the intersection of AI and social justice, highlighting select forthcoming chapter content from real-world experiences and challenges, community-driven solutions, and varied theoretical and computational frameworks. The poster brings together work of librarians, educators, researchers, and community advocates to examine the ethical dimensions of AI and its tangible consequences for marginalized groups. It draws on diverse philosophical, methodological, ontological, and practice-based perspectives to critically evaluate AI’s potential both as a mechanism for empowerment and force capable of exacerbating existing inequalities. Issues of algorithmic bias, inclusive data governance, and culturally responsive AI implementation provide an insightful, timely, and comprehensive examination of AI through the lens of social justice imperatives. The poster provides multidisciplinary and international coverage of ethical frameworks, resistance strategies, educational applications, data autonomy, cultural competence, and equity in information systems design. Algorithmic oppressions in information retrieval, equitable implementation of AI in libraries and educational institutions, culturally responsive AI practices, and community-driven AI initiatives are included.

This paper takes some first steps to trying to define metadata that may curb some issues related to digital ephemera, such as context collapse and information decay. We attempt to show aspects of cultural context as related to how memes (each of which are collections of visual artifacts that carry a cultural idea) are indexed by Google Cloud Vision (GCV). Considering three meme collections that differentiate in their visual content’s expression of an “idea,” we contribute some exploratory metrics. These metrics attempt to account for locations of cultural ideas across web domains using Gini coefficients as a measure of distribution across web domains.

In elementary through higher education, data-driven technologies have long been proselytized as forward-looking answers to the financial problems that stem from neoliberal gutting of public resources (Besser & Bonn, 1996; Eaton, 2022; Newfield, 2016). This project consists of a two-part study about AI in education based on a survey of 500 members of the American Association of University Professors (AAUP) concerning workers’ perspectives on technology deployment in education and 15 interviews with union members, which include graduate students, contingent faculty, full-time faculty, librarians, and staff, to understand how those on the ground conceptualize and prioritize technology issues related to transparency, surveillance, deskilling, and work intensification, and how they see claims of AI’s capacity to increase accessibility.

The intersection of technology and the legal profession has evolved significantly, with legal practitioners using tools like Westlaw, LexisNexis, and Google Search for legal research. More recently, artificial intelligence (AI) tools, such as ChatGPT, have been integrated into legal practice, offering both promise and challenges. In particular, incidents like the Mata v. Avianca case, where attorneys faced sanctions for submitting fictitious citations generated by ChatGPT, highlight the risks of relying on AI in legal work. While courts have issued guidelines for the responsible use of AI, there remains a clear need for due diligence and a thorough understanding of its limitations. This study evaluates the effectiveness of AI tools in legal research by presenting two complex legal questions, one involving U.S. asylum law and the other concerning Colombian legal principles. The responses from ChatGPT and Google’s AI were analyzed for accuracy and consistency, revealing significant gaps in both tools’ performance, particularly in providing complete and current legal information. While they can assist in research, they are not yet reliable enough to replace expert legal analysis. The findings suggest that legal professionals should approach AI-generated legal information with caution and verify results with human expertise.

This study presents preliminary findings on health information professionals’ perceptions of the importance of reliable healthcare information. This study analyzes 336 responses from health information professionals across 61 countries. The results reveal statistically significant differences in perceived needs for health literacy promotion and the enforcement of professional standards and ethics across country income levels. These findings highlight the importance of tailoring equitable healthcare information policies to address global disparities.

Cloud storage is widely used by researchers for collaborative projects, offering clear benefits for data sharing and collaborative work. However, managing shared files in cloud storage can be challenging, especially when collaborators have different personal information management practices. By conducting a grand-tour interview and semi-structured interviews with 30 researchers across disciplines in the United States, this study explored different strategies researchers use for managing shared files and identified three successful cases. These cases revealed effective strategies, including developing a set of rules for managing files, creating a guide file, and providing a training session. In particular, the results showed that even small efforts can significantly enhance the organization and accessibility of research files. This study deepens our understanding of shared file management and provides practical strategies that can support more effective shared file management for collaborative research projects.

This study analyzes two avian metaphors — “Canary in the Coal Mine” and “Pigeon packet”—in cybersecurity discourse. Using Kueffer and Larson’s (2014) metaphor criteria, alongside Ritchie’s (1999) incongruity theory and Koestler’s (1964) concept of bisociation, we examine their factual grounding, rhetorical function, and humor effects. While both rely on incongruity for impact, their effectiveness varies across social and cultural contexts. Findings highlight how metaphor and humor can enhance engagement and risk awareness in cybersecurity communication.

Explaining cybersecurity concepts to the public is often challenging due to their complexity, leading to the wide use of metaphors, but their effectiveness remains under-explored. Building on prior work identifying eight metaphor categories, this study engaged six cybersecurity experts to evaluate their appropriateness and communicative value. Preliminary findings suggest replacing dominant fear-based metaphors like war and crime with health and risk management frames that better promote public engagement and responsible behavior. Experts also offered guidance on crafting effective metaphors, emphasizing contextual clarity, precision, and relatability. By foregrounding key considerations in metaphor design, this study lays groundwork for building more responsible and accessible cybersecurity knowledge infrastructures, contributing to broader efforts in human-centered science communication

This preliminary study explores citizens' perceptions regarding the adoption of artificial intelligence (AI) in healthcare, grounded in Cultural Cognitive Theory. A total of 10,523 responses from the Pew Research Center's American Trends Panel Survey Wave 119 were analyzed using Pearson’s Chi-square test of independence. The findings indicate that citizens’ view, specifically their optimism or skepticism toward AI technology and their ideological stance, are significantly associated with their perceptions for AI use in healthcare.

KEYWORDS

Although generative AI is widely recognized for its potential to enhance team collaboration, unequal access among team members is often overlooked. To explore how this seemingly detrimental uneven AI integration impacts team composition and, consequently, team effectiveness, this study extends the classic Input-Mediator-Output model to an Input-Process-State-Output (IPSO) framework. A lab experiment with 60 two-person teams reveals that unequal AI access yields the highest productivity, improving both task quality and completion time compared to no or full AI access. This effect is driven by two key mechanisms. First, negative socio-emotional interactions and increased cognitive diversity serve as a positive serial mediation pathway linking unequal AI access to enhanced task quality. Second, unequal AI access leads to more concentrated and imbalanced questioning behaviors, accelerating task completion. This study provides a theoretical explanation of how AI integration structures operate in teams and offers guidance for the design and management of human-AI systems.

This paper revisits Kling's (1994) framework for analyzing technology discourse by applying it to expert commentary on Generative AI (GenAI) posted on X (formerly Twitter) from November 2022 to June 2023. Using thematic content analysis on 1,000 top-engaged posts, we assessed how Kling's five genres i.e., utopianism, anti-utopianism, social realism, social theory, and analytical reduction map onto current GenAI discussions. We found that 30% of the discourse fell outside these categories, prompting us to introduce a new genre: Enhanced Technological Realism, which includes Collaborative Tech Assessment (CTA) and Technological Futurism. This addition accounts for the evaluative and speculative tones present in today’s GenAI discourse. Our work updates social informatics theory for the era of decentralized, user-driven online discussions.

As Artificial Intelligence tools become increasingly integrated into library systems, concerns about privacy, security, and ethical implementation have grown alongside their benefits. This study presents a systematic literature review examining AI-powered recommender systems within library contexts, with a focus on their applications, associated risks, and governance. The literature reviewed highlights key security and privacy risks, including data poisoning, inference attacks, and algorithmic manipulation, which threaten core library values such as intellectual freedom and patron confidentiality. Existing policy frameworks, such as the GDPR and the AI Act, offer guidance on fairness and transparency, but a lack of library-specific governance standards persists. A balanced security approach is proposed, consisting of technological safeguards, AI awareness training among staff and patrons, and the creation of library-specific standards.

Technology-supported emotional wellness interventions have gained significant traction in recent years, yet many fail to address the unique needs of marginalized populations whose identities and experiences often go unrecognized in standardized approaches. Drawing inspiration from art therapy’s established success in utilizing creative expression for emotional processing, this research investigates how AI-generated content can serve as a bridge between traditional therapeutic approaches and culturally responsive care. Through a pilot study involving three participants, we identified key challenges and opportunities in leveraging AI-assisted creative tools for therapeutic and expressive purposes. Our findings reveal both technical limitations and promising pathways for developing more inclusive and culturally sensitive AI-powered visual expression platforms.

Diffraction is a well-known term in the context of quantum mechanics, where the nature of light and its behaviors can be observed. Diffraction as a critical research methodology was coined by Haraway (1990, 2013, 2018) and Barad (2003, 2007, 2010) in the context of critical science and technology studies (STS). Unlike reflection, where discourse and abstraction are leveraged to fill in the gaps between knowledge and representation, diffraction acknowledges the ontological inseparability of the material and discursive. While diffractive methodologies are growing in popularity in other critical research areas, there appears to be almost no explicit mention of diffraction in library and information research. One exception is Østerlund et al. (2020), who propose diffraction as a methodology to study trace data. In this poster, I describe diffraction as a methodology and situate my efforts to build on the existing literature and develop a diffractive methodology for critical LIDS research.

In vitro fertilization (IVF) is a common and effective treatment for infertility, requiring patients to access diverse information for informed decision-making. While healthcare professionals provide specialized reproductive advice, many patients seek additional emotional support and shared experiences from peers through online support forums. This study examines how patients undergoing IVF engage in self-disclosure within these forums, focusing on the topics, purposes, and effects of sharing personal information. Through content analysis of 600 posts and 1800 responses, the study reveals that patients frequently disclose information such as age, location, and medication history. The primary motivations for sharing include emotional venting and self-clarification. Patients mainly seek empathy and clarification, especially during critical IVF stages, such as starting treatment or confirming pregnancy. Findings suggest that timely emotional support from healthcare professionals, alongside clinical advice, could enhance patient well-being. Additionally, fostering open information-sharing within patient support groups can offer significant emotional benefits. Platform designers are encouraged to develop user-friendly, privacy-conscious environments that balance information sharing with security. This research highlights the importance of emotional disclosure in IVF communities and suggests ways to improve support structures for patients navigating infertility treatments.

While generative AI (GAI) is widely used in cultural heritage promotion, how humans engage with this technology in relevant artistic creation remains underexplored. This study conducted in-depth interviews to investigate the approaches, challenges and mitigations of human-GAI collaboration in such contexts. We found that human-GAI collaboration includes four stages: (1) creative context setting, (2) GAI tools evaluation, (3) creative concepts formation and (4) human-GAI collaborative production. In this process, two main challenges were identified: (1) cultural misrepresentation and (2) intellectual property. Relatedly, corresponding mitigations were proposed.

This study proposes the Tri-RealitY framework(TRY), an innovative framework for examining how individuals cognitively interpret and respond to misinformation when navigating inconsistent or ambiguous information. Grounded in incongruity theory, the framework identifies three dimensions influencing how individuals cognitively interpret information: General Reality (grounded in common knowledge), Contextual Reality (guided by situational cues), and Ideological Reality (shaped by personal beliefs and social identities). We argued that when individuals encounter inconsistent or ambiguous information, they engage in compensatory cognitive processes across these dimensions to restore interpretative coherence. Successful compensation can lead to the acceptance of contradictory messages, whereas unsuccessful compensation results in disbelief or rejection. Rather than defining misinformation solely by intent or factual accuracy, this framework reconceptualizes misinformation as a case of misaligned acceptance—a receiver’s failure to correctly interpret or compensate for informational inconsistencies. Thus, misinformation can misinformation can arise even when message types are correctly identified, reflecting deeper interpretive misalignments rather than mere factual errors.

This study presents an exploratory mixed-methods study investigating student perceptions of political ideological bias in university classrooms, directly addressing claims made by Turning Point USA's Professor Watchlist. Researchers surveyed undergraduate students at a large southeastern university, defining discrimination as negative impacts due to group membership and propaganda as flawed argumentation used to sway perceptions. While the majority of students reported no bias, those who did often confused normal classroom debates and differences of opinion with discrimination. The study also highlights that Republican students were more likely to perceive course content as discriminatory, whereas liberal students reported experiencing xenophobic language and stigmatization.

Living in a rapidly evolving digital environment, various forms of literacy are crucial for students to navigate the digital world. This study investigates the presence of literacy-related courses within undergraduate programs at iSchools. Using topic modeling, we analyzed 1,507 undergraduate course descriptions from 26 iCaucus schools in the North American region, identifying a total of 22 topics. Two noteworthy findings emerged: 1) only 28 literacy-related courses were identified across 12 institutions, and 2) these courses were scattered across eight different topics rather than forming a cohesive, standalone topic. These results reveal that literacy, in its various forms, is not currently a central focus in the design of undergraduate curricula within iCaucus schools. As a next step, we plan to expand our sample to include courses from iSchools across all membership levels and geo-regions and to further investigate how literacy-related content is integrated into broader curricular structures.

The ability of the public to access and use an Open Government Data Portal (OGDP) is essential for unlocking the potential of Open Government Data (OGD). Through OGDP, the government facilitates public access to its data and, at the same time, supports broader open government initiatives. The accessibility and usability of OGDPs remain critical, as they enable the public to engage with and derive value from OGD. To deepen understanding of these aspects, this study assesses the effectiveness of the OGDP user interface (UI) and user experience (UX) design through an evaluation of the Satu Data Indonesia (SDI) portal, the Indonesian national OGDP. The study involves ten participants in a 30-minute experiment using the SDI portal to complete three tasks, as well as a post-experiment survey. The findings indicate that most participants experienced difficulties in searching for datasets, which negatively affected their willingness to use the portal in the future.

As algorithm-driven content curation becomes an increasingly common feature of short video platforms, it becomes critical to reveal the role of short video algorithms in cultural sustainability for Ethnic Minorities (EMs). This study conducted one-on-one semi-structured interviews with 15 young Tibetan users, and employ the thematic analysis for exploring the findings. Furthermore, the research results indicate the importance of leveraging short video platforms to enhance the visibility and sustainability of ethnic minority cultures, thereby providing a foundation for developing future algorithmic systems that support global cultural communication.

This study presents the results of an action research project exploring racial justice in public library communities and discusses the implications of its findings for AI bias. Two research questions were addressed: “How is racial justice conceptualized by the LIS practitioner community in the American South?” and “What are potential implications of racial justice discourse in race-related AI bias?” Data was gathered from a strategic action plan created from a survey of southern U.S. public library staff that was analyzed using thematic analysis and interpretative description (ID). Public library staff have defined “racial justice” in terms of the constructs of access, equity, participation/inclusion, representation, accountability, and promotion/support. The results of this characterization of racial justice have implications for remediating race-based bias in artificial intelligence systems by promoting the inclusion of more training and operational data gathered from an emic perspective.

The archival and record-keeping practices of the Sisters of Perpetual Indulgence (SPI) reflect a unique intersection of queer activism, roles and structures borrowed from Catholic religious hierachies, and the organizational demands of 501(c)(3) nonprofit work. This study examines how these contexts shape the individual and community information work of members of SPI with an emphasis on their use and management of information and communication technologies (ICTs). Through findings from interviews with members from 10 different SPI houses within the United States, the paper discusses how ICTs inform the information activism of SPI members. Themes discussed in the paper include how SPI members prioritized ICT choice and use, the role of management of house information via ICTs, and how ICTs impacted member accountability across houses. Findings reveal that compliance with nonprofit regulations informed which ICTs members and houses prioritized and, by extension, shaped their advocacy work. Additionally, findings suggest that community accountability helps institutionalize collective management of house digital assets and their use and management. The paper concludes by contending the complex uses by SPI houses and their members reveal ways that ICT providers ought to consider the complexities of activist work to design more adaptable systems, while supporting nonprofit.

This study investigates how affect shapes and is shaped by archival narratives, focusing on identity formation in grassroots archives. Using the Picun Culture and Arts Museum of Migrant Labour (PCAMML), a community museum established by Chinese migrant workers as a case study, it explores the dual role of affect: both as a structuring medium guiding curatorial practices and as a product evoked among visitors. Drawing on structuration theory and affect theory, the research employs qualitative methods, including curator interviews, thematic analysis of exhibition materials, and audience feedback from guestbooks and social media. Findings reveal that curatorial decisions were driven by affective motivations such as the desire for belonging, historical redress, resistance to stigma, and the imagining of alternative futures. In turn, visitors reported experiencing affective resonance, strengthened identification with migrant workers, and reflexive awareness prompting social critique and potential action. The study contributes theoretically by conceptualizing archives as affective assemblages that mediate social memory and identity, and practically by highlighting the role of affective storytelling in empowering marginalized communities and fostering inclusive historical narratives.

The Trump administration’s erasure of public data—climate science, CDC records, HIV research—exposed the archive as a contested site where knowledge is shaped, erased, and reconstructed by power. In response, archivists and activists are engaging in salvage archiving to preserve endangered records in shadow archives. This paper examines how AI could be repurposed for counter-surveillance, systematically tracking government data erasure, to prevent future loss. However, as scholars rooted in Indigenous feminist methodologies, we must critically interrogate not only the dangers of AI co-optation by techno-oligarchs like Elon Musk, but also AI’s material impacts—its land use, energy demands, and reliance on extractive labor. Even in the unlikely event that AI escapes corporate capture, we must ask: at what cost to land, and at what cost to future generations? This paper ultimately positions AI not as a revolutionary fix, but as a modest, deeply flawed tool that can only be part of a much larger project of archival sovereignty—one that must remain accountable to land, labor, and the communities AI is meant to serve.

The integration of Artificial Intelligence (AI) into religious contexts has sparked considerable debate, raising theological, ethical, and epistemological concerns. This study employs a qualitative approach to examine how individuals navigate religious and spiritual information in increasingly complex digital environments. Specifically, it investigates AI’s role in delivering religious sermons by analyzing a real time YouTube videos and its reception, drawing insights from nearly 1000 comments. The study explores public perceptions of AI-driven religious discourse, assessing its implications for information practices, belief systems, and spiritual engagement.

Grounded in theories of information practices, this research examines how AI-mediated religious content disrupts and reshapes established spiritual information landscapes. It cross-examines how individuals engage with and interpret AI-generated sermons, addressing broader concerns related to religious authority, authenticity, and technological mediation.

Findings reveal strong opposition to AI-driven sermons, rooted in concerns about biblical prophecy, the perceived absence of a soul in AI, fears of AI worship, and a general distrust of technology in spiritual domains. However, a minority acknowledges potential benefits, including improved accessibility, enhanced religious education, and administrative efficiency. The study underscores prevailing anxieties regarding AI’s role in faith-based practices, highlighting the need for ethically informed, spiritually sensitive approaches to AI integration in religious contexts.

Artificial Intelligence (AI) has become prevalent in all sectors of society, including higher education institutions. Many studies have examined iSchools curricula, focusing on areas such as data science, digital humanities, and archival studies. However, few studies have examined AI education at iSchools in the United States (US) and Canada. Research is needed to address AI in information science (IS) education, fueling the conversation about AI across the iSchools' curricula. This study analyzed the AI-related courses in graduate and undergraduate programs offered by members of the iSchools organization in the United States and Canada. We identified the area(s) and facet(s) covered in each course title and coded them. Of the 51 iSchools, twenty-nine offered AI-related courses. The most covered areas include general AI, Machine Learning, Natural Language Processing, Deep Learning, and Robotics. Most courses focus on AI's technical and applied facets, while a few cover the ethical, societal, cultural, and legal facets. Implications include the need for iSchools to offer AI courses that cover aspects beyond the technical, more undergraduate courses, and certificate programs that contribute to educating the labor force that needs upskilling. This study informs the iSchools' curricula strengths to build on,gaps to fill, and IS practice.

The widespread adoption of chatbots and large language models has significantly impacted various aspects of daily life. This study employs mixed methods to analyze ChatGPT logs from 36 undergraduate students, providing a comprehensive examination of how this technology is integrated into academic contexts. ChatGPT had diverse applications with the most prevalent uses centering on essay writing assistance. We identify more dynamic scenarios, such as students utilizing ChatGPT to generate and learn computer code across multiple programming languages. The study explores the evolving parasocial relationship between students and ChatGPT, particularly focusing on conversational repair processes and how these interactions change over time. Building upon previous research in human-chatbot interactions, we offer insights into the nuanced ways students engage with AI-powered language models. These findings inform a set of design recommendations aimed at enhancing future chatbot interactions and contributing to theongoing discourse on the role of AI in education and beyond.

This systematic review explores the potential of generative artificial intelligence in supporting neurodivergent students in higher education. Drawing on 21studies published between 2022 and 2025, the review synthesises current evidence on the challenges faced by neurodiverse learners, the capabilities of generative AI tools, application areas, and implementation barriers. Findings show that generative AI tools can support academic writing, task planning, emotional regulation, and self-management by providing personalised, flexible, and multimodal support. Positive outcomes include improved writing fluency, time management, self-efficacy, and engagement. However, challenges remain regarding personalisation, accessibility, and ethical use. The review identifies emerging conceptual dimensions such as cognitive regulation, emotional support, and identity formation, and calls for stronger theoretical frameworks, inclusive design practices, and sustained institutional support to ensure equitable use of generative AI in higher education.

Generative information retrieval (GenIR) systems represent a paradigm shift in interpreting and addressing user needs, yet their effectiveness remains constrained by limited interaction design capabilities for accurate intent understanding. Recognizing the connection between search intentions and task characteristics, this study identifies two categories of task characteristics that can inform the interaction design of GenIR systems: (a) general task characteristics, which consistently influence search behaviors across diverse contexts and user groups, and (b) specific task characteristics, which are particularly influential in certain contexts or among specific user groups. To identify these characteristics, the study quantitatively synthesizes existing research through a meta-analysis of 22 experimental studies. The findings suggest that task difficulty, task urgency, and task sources are key general task characteristics. However, task goal specificity is more influential in learning contexts, while task complexity predominates in daily life and healthcare contexts, as well as for middle school students. Based on these findings, this study develops a task-aware interaction design framework that strategically guides users in articulating both general and specific task characteristics through iterative dialogues. This design optimizes intent understanding by achieving: (1) operational efficiency through the analysis of general characteristics, and (2) personalized adaptation through the interpretation of specific characteristics.

People shopping online often abandon their shopping sessions because they feel overwhelmed or insufficiently supported during product searches. We instructed 31 participants to perform two goal-directed product searches online, simulating real-world scenarios for two product types: search products (laptops) and experience products (jackets). Through observation and think-aloud protocols, we captured user behavior across browser tabs and online resources, enabling us to develop a novel annotation scheme for product search that captures resources used, views seen, and actions taken. Qualitative analysis of these annotated sessions revealed nine distinct product search strategies, which participants often combined and applied at different stages of their search sessions. For each strategy, we describe similarities and differences between search and experience products and identify common strategy combinations across product types. Finally, by mapping these findings to established information-seeking models, we offer insights that can inform the design of more effective and supportive e-commerce platforms.

Expectation confirmation in data retrieval systems remains a critical yet understudied issue. To bridge this gap, this paper proposes a multi-dimensional framework for investigating the confirmation formation in the context of data retrieval, drawing upon the Expectation-Confirmation Theory and expectancy-value theory. Furthermore, it introduces a deep-learning model called Multiple-Expert System based on Bayesian Neural Networks (MES-BNN) to predict multi-dimensional confirmation by analyzing users’ search behavior data. The findings reveal the multi-dimensional and context-dependent nature of confirmation. In the data retrieval context, users discern gaps between their search experiences and expectations in terms of task, cognition, and emotion, collectively forming a three-dimensional confirmation. This three-dimensional confirmation can be predicted through search behavior data mining. Furthermore, the MES-BNN model demonstrates its effectiveness in mining small-scale behavioral data, enabling automatic and accurate prediction of the three-dimensional confirmation and contributes to advancing data analytical approaches employed in user-oriented retrieval studies in the intelligent age.

Generative artificial intelligence (GenAI) is rapidly transforming how human beings perform cognitive and creative tasks, including the strategies they employ in seeking information. Freed from the constraints that have traditionally shaped query formulation in traditional query-response information retrieval (IR) systems, GenAI users employ novel strategies—framing commands in natural language, embedding personal details, and experimenting with conversational approaches. Drawing on information-seeking research in library and information science (LIS), the present study examines the structure and defining features of these human–GenAI interactions, revealing notable parallels with Belkin’s (1980) Cognitive Communication System for Information Retrieval. In doing so, it underscores essential implications for contemporary information retrieval in an era of expanding global GenAI adoption.

This paper reports on the use of chatbots among US-based, national sexual assault and domestic violence hotlines, and examines how these chatbots are conceptualized. Because chatbots are indifferent to their outputs but survivors in crisis/danger require trauma-informed, empathetic support, it is important to understand how hotlines conceptualize their chatbots and present them to survivors. Through qualitative content analysis of supporting documentation about the chatbots, this project found that the hotlines describe similar purposes of the chatbots and state the chatbots are not replacements for live services. However, the hotlines diverge in how they refer to the chatbots (“it” vs. “she” / “digital friend”) and in what features the chatbot offers. The use of “she” and “digital friend,” and the knowledge and skills required by some features of the chatbots blur the line between the chatbot being a simple, information retrieval tool and human-like enough to replace trained, live support.

Palliative care is frequently misunderstood, yet short videos on social media can help disseminate useful information and build supportive communities. One major challenge is that manually analyzing such content is labor-intensive and time-consuming. Meanwhile, large language models (LLMs) show promise for automated content analysis, but their domain-specific accuracy in this sensitive area remains uncertain. In this study, we propose an iterative LLM-LLM agentic conversational approach to identify palliative care themes from 56 TikTok videos. We collected video transcripts, metadata, visual labels, and on-screen text to build a multimodal dataset. Through iterative dialogues between two LLMs, we generated initial themes and refined them via human feedback to address missed dimensions. Our approach identified themes such as Policy, Advocacy, and Access, as well as Emotional Support and Coping while highlighting omissions like Humor and Saying Goodbye, underlining the need for human oversight. Our findings reveal that LLM-driven automation can reduce annotation workload, but it has limitations in capturing emotional content. The contributions of this work include a new annotated dataset of 242 TikTok videos, a validated LLM-based thematic analysis pipeline, and evidence that combining automated and human-in-the-loop methods enhances reliability and accuracy in short-form video analysis.

The growing use of Generative AI (GenAI) conversational search tools has raised concerns about their effects on people’s metacognitive engagement, critical thinking, and learning. As people increasingly rely on GenAI to perform tasks such as analyzing and applying information, they may become less actively engaged in thinking and learning. This study examines whether metacognitive prompts—designed to encourage people to pause, reflect, assess their understanding, and consider multiple perspectives—can support critical thinking during GenAI-based search. We conducted a user study (N=40) with university students to investigate the impact of metacognitive prompts on their thought processes and search behaviors while searching with a GenAI tool. We found that these prompts led to more active engagement, leading students to explore a broader range of topics and engage in deeper inquiry through follow-up queries. Students reported that the prompts were especially helpful for considering overlooked perspectives, promoting evaluation of AI responses, and identifying key takeaways. Additionally, the effectiveness of these prompts was influenced by students’ metacognitive flexibility. Our findings highlight the potential of metacognitive prompts to foster critical thinking and provide insights for designing and implementing metacognitive support in human-AI interactions.

We are reaching an era where the public increasingly relies on large language models (LLMs) for information on current events. Existing research on the subject asks LLMs to take a political stance through survey questionnaires, persona adoption, or multiple-choice prompting. The following research examines the implicit political lean of LLMs when responding in natural language to queries on 77 topics that were of public interest from 2017 to 2021. Four LLMs were prompted using two natural prompting styles, resulting in 808 unique responses. Responses were human annotated to identify topics that LLMs redirected or outright refused to answer, and were classified via a neural network as conservative, moderate, or liberal. Further, LLM responses were analyzed on paradigms of whether topics were polarized, international, or asked using non-neutral language. Findings suggest that these LLMs lean moderate to liberal, erroneously refuse neutral topics, and are inconsistent in answers to the same prompts. These findings illustrate the risk of relying on generative AI for answers in an increasingly polarized environment and call for information professionals to examine and discuss implicit misinformation in the age of AI.

Generative AI (GenAI), as exemplified by ChatGPT, is transforming the way people seek information and interact with information systems and resources. This study investigates users’ prompt formulation behavior through a longitudinal observation of experienced ChatGPT users. Extending prior research on prompt engineering, this study introduces the IIOCR framework, delineating five core components: input, instruction, output, context, and relation. The findings reveal that users have a strong preference for simple prompt, with single-component prompts accounting for 43.8% of all prompts. Dual-component combinations constitute 38.2%, with Input + Instruction (20.2%) being the most frequent pattern. Only 18.0% of prompts involve multi-component combinations, indicating that complex prompt formulations are infrequent in typical user interactions. The IIOCR framework reveals users’ preference for simplicity and directness. It also offers practical insights for user-centered AI design by emphasizing the instruction, input, and output components that address users’ core needs.

In the era of generative artificial intelligence (AI), the skill of effectively crafting prompts referred to as prompt literacy—has become increasingly vital. Despite its significance, there remains a paucity of research delineating the attributes that constitute a proficient prompter. This study introduces a comprehensive, multidimensional framework to assess prompt literacy, encompassing cognitive, experiential, and sociocultural dimensions. To empirically investigate this framework, we conducted a mixed-methods experiment involving 60 participants aged 18 to 35. Throughout these sessions, participants' eye movements were meticulously recorded using Tobii Spark eye-tracking technology. Complementing this quantitative data, we employed think-aloud protocols to gain insights into participants' cognitive strategies during the prompting process. Post-task, participants completed detailed questionnaires assessing their demographics, AI usage habits, emotional responses, and perceived task difficulty. Semi-structured interviews were also conducted to delve deeper into their prompting strategies and reflections. Our analysis revealed distinct prompting typologies, each characterized by unique behavioral signatures and eye-tracking markers. These findings offer nuanced insights into the competencies that underpin effective prompting in generative AI contexts.

With the rising popularity and ethical concerns about generative artificial intelligence (GAI), there are strong interests in understanding the factors behind its perception and use. Epistemological beliefs, found pertinent to information behavior, are rarely studied in GAI usage research. This study conducted path analysis on survey responses from 322 U.S. adults to explore how epistemological beliefs (from Schommer’s Epistemological Questionnaire) relate to GAI perception (familiarity and perceived likelihood of GAI issues) and use (frequency and seriousness). The study found direct positive paths from Avoid Ambiguity beliefs to perceived GAI issues likelihood and usage frequency, and indirect positive paths from Depend on Authority to frequency and seriousness of use via familiarity with GAI. Theoretical implications and practical implications for information literacy are discussed.

In 2023 Meta launched generative AI (GenAI) features called “Meta AI” for general search, and text-to-image creation in its family of apps including the superapp, WhatsApp. Currently, we do not have empirical research on how people are using these GenAI features in WhatsApp. To understand current information practices with Meta AI, we conducted an interview and task-based study with 26 Indian students at a large public university in the United States. We find that information seeking, planning activities, and image creation are the largest use cases of Meta AI. Participants described relying on external URL links for fact checking and planning tasks as markers of credibility for their search tasks. We argue that Meta’s platform partnerships are likely to influence the kinds of search results participants receive and rely on. Our key contribution to information science is urging researchers to expand sites of studying personal information practice with the adoption of GenAI features based searching activities and consider searching activities that occur within superapps like WhatsApp.

People with disabilities (PWD) and their family members often find it difficult to find information about available services. One of the approaches to address this information access problem is by understanding the ecology of available information sources. However, identifying the landscape of information sources is challenging due to the variety of sources and their varying visibility. This study proposes a computational approach to processing open-ended survey answers by constructing a hierarchical taxonomy of information sources. We developed a semi-automated, LLM-based framework to build a taxonomy of information sources from open-ended survey answers. The resulting 3-tier taxonomy captures broad categories and fine-grained entities, supporting multi-level analysis of information sources. This work explores the feasibility of LLM-based taxonomy building and offers a scalable framework for processing open-ended texts.

This study examines learners' acceptability of digital fact-checking nudges from the dual process theory by evaluating learners’ perceptions towards two types of digital fact-checking nudges comprising heuristic processing (System 1) versus systematic processing (System 2) and the impact of learners’ profiles on the perception of digital fact-checking nudges. The study surveyed and analyzed GenAI usage behaviors and perception towards GenAI digital nudges for fact-checking of 300 students in higher education. Overall, results indicate that learners perceived System 1 nudges to be more effective. While participants’ academic discipline did not significantly affect their acceptability and effectiveness perception towards both nudge types, their Gen AI usage frequency had a significant impact on nudge perception. Avid Gen AI learners have a more positive perception towards nudges, especially System 2 nudges. In terms of theoretical contributions, the study addresses the gap in cognitive processing research for nudge design for learning and fact-checking Gen AI responses. As for practical contributions, the study offers insights for designing effective fact-checking nudges depending on the level of usage and familiarity with GenAI.

The impact of AI on information environments has prompted questions around its ethical regulation, and alignment with the EU AI Act is increasingly necessary. As the first AI regulation in the world, combined with the Brussels effect, the EU is a global AI regulatory leader. This context is compounded by the volatility of other global powers. Information sciences can make unique contributions to policy development with its focus at the intersection of information, technology, and people. This paper reports on the second phase of a project, initiated in 2023, analyzing ethical similarities and differences between the EU’s Ethics Guidelines for Trustworthy AI and the US’ AI Bill of Rights, using qualitative content analysis. Findings demonstrate that ethical differences can be resolved while accounting for similarities. Implications suggest collective need for international cooperation and compliance. This paper provides a case study for detailed info-ethical analysis for regulatory alignment.

Cyberdeviant behaviors refer to deviant information behaviors in digital environments. In organizational contexts, it specifically refers to employees’ intentional actions that violate organizational norms regarding the seeking, sharing, producing, or use of information. Despite its significance, this phenomenon has received limited attention in Library and Information Science (LIS) research. This study adopts a mixed-methods approach to examine how Confucian duty ethics shape cyberdeviant behaviors. Study 1 develops and validates a measurement scale capturing four dimensions of Confucian duty ethics. Study 2 employs Latent Profile Analysis to identify distinct user ethical profiles based on proximal and distal duties. Results reveal that employees characterized by strong distal but weak proximal duty are more likely to engage in cyberdeviant behaviors. This study contributes to LIS literature by introducing a culturally grounded ethical framework and highlighting the divergent roles of proximal and distal duties in shaping deviant information practices.

This paper explores the role of metadata in the repatriation of cultural artifacts, emphasizing the ethical and practical challenges posed by current documentation practices in museums and cultural heritage institutions. While metadata serves as a critical tool for managing collections, its limitations, particularly in documenting acquisition and provenance, create significant gaps that hinder efforts for meaningful repatriation. Drawing on examples from the Spurlock Museum and the Smithsonian American Art Museum, the paper highlights how the absence of dedicated fields for acquisition information can obscure the complexities of ownership, power dynamics, and colonial histories. The paper also examines the rise of digital repatriation, discussing its potential to complement physical returns but also its limitations in addressing broader cultural and ethical issues. The paper argues for the development of an ethical framework for repatriation grounded in four principles: incorporating provenance into published metadata, transparency and accountability, collaboration and inclusivity, and ethical stewardship and long-term commitment. By expanding the concept of provenance beyond ownership, cultural heritage institutions can contribute to restorative justice and reconciliation, ensuring that repatriation efforts are not only legally sound but also ethically grounded in respect for cultural sovereignty.

This study examines how four prominent large language models (Claude 3.7 Sonnet, GPT-4o, Gemini 2.5 Flash, and Deepseek-V3) handle sexually oriented requests through qualitative content analysis. By evaluating responses to prompts ranging from explicitly sexual to educational and neutral control scenarios, the research reveals distinct moderation paradigms reflecting fundamentally divergent ethical positions. Claude 3.7 Sonnet employs strict and consistent prohibitions, while GPT-4o navigates user interactions through nuanced contextual redirection. Gemini 2.5 Flash exhibits permissiveness with threshold-based limits, and Deepseek-V3 demonstrates troublingly inconsistent boundary enforcement and performative refusals. These varied approaches create a significant "ethical implementation gap," stressing a critical absence of unified ethical frameworks and standards across platforms. The findings underscore the urgent necessity for transparent, standardized guidelines and coordinated international governance to ensure consistent moderation, protect user welfare, and maintain trust as AI systems increasingly mediate intimate aspects of human life.

Large language models (LLMs) are increasingly used to assist computational social science research. While prior efforts have focused on text, the potential of leveraging multimodal LLMs (MLLMs) for online video studies remains underexplored. We conduct one of the first case studies on MLLM-assisted video content analysis, comparing AI’s interpretations to human understanding of abstract concepts. We leverage LLaVA-1.6 Mistral 7B to interpret four abstract concepts regarding video-mediated self-disclosure, analyzing 725 keyframes from 142 depression-related YouTube short videos. We perform a qualitative analysis of MLLM’s self- generated explanations and found that the degree of operationalization can influence MLLM’s interpretations. Interestingly, greater detail does not necessarily increase human-AI alignment. We also identify other factors affecting AI alignment with human understanding, such as concept complexity and versatility of video genres. Our exploratory study highlights the need to customize prompts for specific concepts and calls for researchers to incorporate more human-centered evaluations when working with AI systems in a multimodal context.

This study considers ChatGPT as an information source, investigating the information needs that people come to ChatGPT with and the information practices that ChatGPT supports, through a qualitative content analysis of 205 user vignettes. The findings show that ChatGPT is used in a range of life domains (home/family, work, leisure, etc.) and for a range of human needs (writing/editing, learning, simple programming tasks, etc.), constituting the information needs that people use ChatGPT to address. Related to these information needs, the findings show six categories of information practices that ChatGPT supports: Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. This work suggests that, in the AI age, information need should be conceptualized not just as a matter of “getting questions answered” or even “making sense,” but as skillfully coping in the world, a notion that includes both understanding and action. This study leads to numerous opportunities for future work at the junction of generative AI and information needs, seeking, use and experience.

This study investigates how different music discovery approaches influence users’ perceptions of playlist diversity and satisfaction, with a particular focus on both objective and subjective measures of musical diversity. A within-subjects experiment involving 144 Spotify users compared two systems: a user-driven seed-based search and the algorithm-driven Discover Weekly. Objective diversity was calculated through genre, artist, and Spotify audio features, while subjective diversity was measured using participants’ self-reported ratings.

Results from structural equation modeling and t-tests revealed that Discover Weekly consistently produced more diverse playlists across all objective measures. However, only genre and artist diversity significantly influenced users’ perceived diversity; sonic diversity had a limited perceptual impact. Moreover, subjective diversity, rather than objective diversity, showed a stronger association with overall playlist satisfaction. These findings suggest that users’ evaluations are shaped more by perceived categorical diversity than by measurable acoustic variance.

Although familiarity influenced individual track preference, its effect on overall playlist satisfaction was weaker, implying the role of other mediating factors such as novelty or user traits. Overall, our findings reaffirm the need for exploration-oriented measures that go beyond accuracy. Specifically, they underscore the importance of diversity in evaluating playlist-based music recommendations.

The exponential growth of data across diverse domains highlights the need for efficient methods in discovering relevant datasets. Traditional search engines such as Google, have served as the go-to tools for this purpose. Recent advancements in large language models (LLMs) such as ChatGPT and Microsoft Copilot have sparked interest in their potential to serve as alternatives for data discovery. While these models are primarily designed for conversational interactions, their capabilities in information retrieval and dataset discovery are becoming areas of active exploration. In this work, we present a mixed-method study that investigates the difference in user experience when using Google and Microsoft Copilot to search for datasets. This study aims to uncover the strengths and limitations of LLMs in data discovery, offering insights into their potential as alternatives or complements to traditional tools.

During public health events, social media platforms serve as key channels for disseminating multimodal information especially from government departments. These data are crucial for enhancing public understanding and emergency preparedness. This study proposes a novel framework for constructing and learning representations from social heterogeneous information networks (SHINs) based on multimodal fusion and an Enhanced-HGCN (Hyperbolic Graph Convolutional Network) model. Approximately 74,403 flu-related microblog posts were collected, from which multimodal features were extracted to construct a heterogeneous network linking users, posts, and topics. Furthermore, the Enhanced-HGCN model with a two-layer graph convolution structure is proposed to learn node embeddings in the SHINs. Experimental results show that our approach significantly outperforms other baseline models including in clustering performance. This research validates the feasibility of multimodal SHINs construction and the effectiveness of the Enhanced-HGCN, providing a foundation for future applications such as knowledge recommendation and cross-platform information collaboration.

The rapid expansion of content presents significant challenges in records management, notably in retention and disposition, appraisal, and organization. Our study highlights how integrating artificial intelligence (AI) into archival science can help address these issues. We begin with a thorough analysis of 45 papers published between 2011 and 2023 that met our predetermined criteria. All the articles were written in English; 40% of these were reviews, and the remaining 60% were original research articles. We investigated the key AI techniques and their applications in archives and records management functions. Our findings highlight key AI-driven strategies that promise to streamline recordkeeping processes and improve data retrieval in the immediate future. This review outlines the current state of AI in archival science and records management and lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced interdisciplinary collaboration between AI experts and archival professionals.

Provenance documentation is essential for establishing the authority and trustworthiness of objects and data in different domains. However, provenance stories can be susceptible to misinterpretation, incompleteness, or biases, resulting in provenance failures. Extant research has attributed provenance failures to the absence or incorrectness of data. In this paper, we explore the idea of provenance failures and investigate the different types of failures in relation to our typology of provenance uses in research publications. Via case studies in natural history, scholarly communication, and cultural heritage, we posit that provenance failure is an underexplored and undertheorized concept and lay the groundwork of a theory of provenance failure.

Will continuous wars change social morality? While previous research focused on war ethics, in this study, by tracking large-scale historical newspapers, from the perspective of digital humanities, we provide evidence through big data analysis to answer this question. Leveraging the database the Late Qing and Republican-Era Chinese Newspaper and widely-used psychological moral lexicons, we retrace the diurnal dynamics of collective moral motivation in Chinese modern history from 1919/1/1 to 1949/9/30. Analyzing historical newspapers with moral lexicons, we track moral motivation across four wartime periods: Warlord Era, Agrarian War, Anti-Japanese War, and Liberation War. Statistics shown that moral motivation of Chinese society continuously increased as the wars dragged on. It is also discovered that the wars significantly motivated a higher level of agency motive than communion motive. As far as we know, this is the first empirical study from a digital humanities perspective that discusses the relationship between war and collective morality.

This paper proposes Humanities-in-the-Loop, a methodological framework that embeds close reading into each stage of the Retrieval-Augmented Generation (RAG) pipeline to enhance the processing of digital archival materials. This framework includes manual annotation, knowledge maintenance, reviewer validation, prompt engineering, and human interpretation. Taking the diaries of Coching Chu as a case study, the system addresses the limitations of conventional RAG methods in capturing the contextual complexity and historical nuance inherent in personal archives. An evaluation further confirms the effectiveness of this approach in delivering faithful, contextually grounded responses. The proposed framework not only enhances answer accuracy and interpretability but also enables traceable, human-centered inquiry in digital humanities research.

Health information behavior research occupies a central position within the field of Library and Information Science (LIS), given its emphasis on the complex interaction between health-related information, human, and technology. While health information behavior research has been steadily progressing over time, it has undergone significant evolution with the advancement of digital technologies such as artificial intelligence. The goal of this panel is to bring together scholars who have actively contributed to health information behavior research, with a specific focus on the integration of Artificial Intelligence (AI) into health-related information and information behavior settings. Additionally, we will present our research on the current landscape of health information behavior, the applications of AI in researching health information behavior, its limitations, and its societal impact. These insights will spark discussions among participants and help foster a deeper understanding of the opportunities and the responsible use of AI technologies in health information behavior research.

Despite smartphones becoming essential tools for digital engagement, English learning adult immigrants (ELAIs) face significant, yet underexplored, barriers to digital inclusion. This participatory action research study examines the smartphone use of Ethiopian immigrants in the Washington D.C. metropolitan area, focusing on the intersections of language, technology design, and digital literacy. Partnering with a trusted Ethiopian community-based organization (pseudonym: “Andinet”), community members collaborated as co-researchers to investigate how Ethiopian ELAIs navigate smartphone use and overcome challenges. Study findings highlight systemic design inequities and propose actionable interventions, such as Amharic speech-to-text tools, guided digital literacy modules, and community-led training workshops. These insights lay the groundwork for future research and the co-development of culturally relevant technology solutions alongside the community, emphasizing the transformative potential of participatory research and design practices to empower underserved immigrant populations.

The rapid advancement of artificial intelligence (AI) is reshaping various industries, including academic libraries, which are actively engaging with AI. This study examines AI engagement in 120 academic libraries across six regions using scoping review and thematic analysis. It identifies two categories and five subcategories: (1) User-oriented services (AI guidance, AI literacy, and AI tools) and (2) Institution-oriented development (AI research and strategy, AI implementation and innovation). AI literacy serves as the foundational core, AI tools and AI guidance act as key pillars, and AI research and strategy, along with AI implementation and innovation, function as driving engines. Regionally, North America demonstrates advanced AI adoption, Oceania exhibits high engagement, Europe, Asia, and Africa contribute to steady integration, while Latin America shows strong potential in early exploration. Notable AI applications include AI chatbots, AI OneSearch Lite, and Digital Collections AI. These findings offer valuable insights for academic libraries and policymakers.

Information searching plays a critical role in idea generation. However, the connection between search behaviors and creative thinking processes remains underexplored. This study investigates how creativity support search tools guide idea generation during divergent and convergent thinking tasks by comparing a visual-based and a text-based search tool. Based on think-aloud protocols and interviews with 58 participants, we found that the two tools supported different aspects of the creative thinking processes. The visual-based tool fostered iterative engagement, helping users revisit, reorganize, and make idea connections, while the text-based tool encouraged more linear and straightforward strategies such as note-taking. Divergent thinking led to broad, exploratory searching and flexible relevance judgments, whereas convergent thinking prompted early filtering and focused evaluation. These findings reveal the cognitive complexity in transitioning from search to idea generation, and highlight the potential for search systems to better support creativity by scaffolding key stages of the idea generation process.

This study examined a potential paradigm shift in online health information search in the era of Generative Artificial Intelligence (GenAI). We systematically searched relevant literature, selected eligible studies using preset inclusion and exclusion criteria, extracted and analyzed data. A total of 87 studies were included and examined across four research questions. We identified a significantly growing global interest in understanding and utilizing GenAI in health sciences, compared with traditional search engines (e.g., Google, Bing) and resources (e.g., human expert resources, clinical guidelines, organizational websites, and medical databases). This bibliometric survey highlights the potential and general positive sentiment of integrating GenAI tools like ChatGPT into healthcare information search, services, and research. Future research shall continue monitoring the dynamic and evolving landscape, exploring the opportunities and challenges of utilizing GenAI tools and features to ensure responsible applications and meet the information needs of all users.

Many COVID long-haulers have faced barriers to obtaining the information and support they need. As a result, they often turn to the stories others with long COVID have shared online and off, frequently sharing their own stories as well. We conducted a mixed-methods investigation into the information experiences of COVID long-haulers, seeking to understand the strategies they use to meet their information needs and the barriers they encounter. We surveyed 135 COVID long-haulers and conducted 29 follow-up interviews, focusing on the information-related aspects of their long COVID experience. In this paper, we report findings related to participants’ perceptions about the usefulness of reading other people’s stories and participants’ reasons for sharing (or not sharing) their own stories. Findings reveal that reading and sharing stories of personal experiences related to COVID/long COVID is very prevalent, and people have specific, though varying, reasons for engaging (or not) in these activities.

Guided by the dual-processing model of information credibility, this systematic literature review analyzes how young adults evaluate the credibility of social media influencer (SMI) content, synthesizing findings from 30 peer-reviewed studies published between 2015 and 2024. Findings show that credibility judgments are shaped by user motivations (e.g., social influence, platform dynamics), abilities (e.g., digital literacy, domain knowledge), and heuristics (e.g., source reputation, aesthetics). Eleven key constructs of SMI content credibility are identified, grouped under trustworthiness (e.g., honesty, relatability) and expertise (e.g., competence, authority). SMIs emerge as both trusted health communicators and potential sources of misinformation. The review emphasizes the importance of promoting digital literacy, promoting transparent platform design, and implementing regulatory safeguards to facilitate young adults’ accurate assessment of health information online.

In Saudi Arabia, mothers of autistic children face difficulties accessing accurate information and reliable support due to limited services and social stigma. This paper presents early findings from an ongoing qualitative study exploring mothers' information behaviour, including how they seek, evaluate, and use autism-related information via social media and AI tools. Based on interviews with six mothers, the study shows how social media provides emotional validation, peer support, and practical insights. Mothers evaluate content through repetition, personal experience, and ChatGPT, which is used to verify, simplify, or explain information. Alexa also became a communication aid for a blind autistic child. These findings reveal how mothers actively manage uncertainty and compensate for gaps in professional support. The paper contributes to understanding autism-related information behaviour in a Saudi context. It highlights the need for more accessible, accurate digital resources for underserved families lacking trained professionals, public awareness, and inclusive education options.

The potential for harm caused by deepfakes has been widely recognized, with concern over its psychological, social, and political consequences. This study investigates how individuals from different age groups perceive the harms that deepfakes could bring through the lens of the third-person perception (TPP). An online survey was conducted with 132 young adults and 152 seniors on perceptions of physical, emotional, financial, societal, and relational harm. The findings reveal significant TPP across all harm types, with seniors exhibiting a stronger TPP effect than young adults. Our findings extend TPP research into the examination of deepfake harms, as well as calling for tailored and age-sensitive media literacy interventions and risk communication strategies.

Public discourse around climate change remains polarized despite scientific consensus on anthropogenic climate change (ACC). This study examines how “believers” and “skeptics” of ACC differ in their YouTube comment discourse. We analyzed 44,989 comments from 30 videos using a large language model (LLM) as a qualitative annotator, identifying ten distinct topics. These annotations were combined with social network analysis to examine engagement patterns. A linear mixed-effects model showed that comments about government policy and natural cycles generated significantly lower interaction compared to misinformation, suggesting these topics are ideologically settled points within communities. These patterns reflect motivated reasoning, where users selectively engage with content that aligns with their identity and beliefs. Our findings highlight the utility of LLMs for large-scale qualitative analysis and highlight how climate discourse is shaped not only by content, but by underlying cognitive and ideological motivations.

As AI-generated content (AIGC) becomes increasingly prevalent across digital platforms, understanding its impact on users' trust and attitudes toward this new technology is crucial. Using an experimental design, we examined how the presence or absence of disclosing the use of AI in content generation influences user engagement with AI-generated images and videos on social media platforms. Our experiment recruited 64 individuals (N control group =31 , N experiment group =33) from various social groups, with experiment group being explicitly informed of the usage of AI in content generation and control group not informed. User’s behavioral variances are captured and measured using eye-tracking devices during the experiment session. We also conducted interviews with participants to further explore their experiences and attitudes. The findings highlight the nuanced role of disclosure of AIGC in shaping user trust and offer practical implications for the ethical presentation of AI-generated content.

Despite industrial initiatives and government regulations to ensure fairness in AI, gender bias remains a concerning issue, causing bad user experience, injustices, and mental harm to women. Computing education has incorporated ethics discussions to prepare students to design more ethical AI systems. However, through interviews with 18 AI practitioners/learners, we revealed limitations of the current gender bias education in the computing curricula – the education is absent, sporadic, abstract, or tech-oriented. We designed and evaluated hands-on tutorials to raise AI practitioners/learners’ awareness and knowledge of gender bias – such tutorials have the potential to complement the insufficient education on AI gender bias in computing/AI courses. By reflecting on the lessons from the design and evaluation process, we synthesized design implications and a rubric to guide future research, education, and design.

This study explores the use of handheld whiteboards as a low-tech, active learning strategy in a large undergraduate Information Science course. Grounded in Cognitive Apprenticeship and Situated Learning theories, the study investigates how whiteboard-based activities support student engagement, peer collaboration, and visualization of abstract concepts. Using a mixed-methods approach, survey responses from 184 students revealed that most participants perceived the whiteboard exercises as helpful for enhancing participation and conceptual understanding. Thematic analysis of qualitative feedback highlighted benefits such as increased attentiveness, real-time feedback, and peer learning, alongside challenges related to note-taking and anxiety. The study contributes to a deeper understanding of how simple, physical tools can complement technology-driven instructional strategies and inform inclusive, scalable approaches in information science education.

The debut of Generative AI (GAI) tools in late 2022 profoundly unsettled higher education institutions (HEIs), including the iSchools. This exploratory study is the first to scrutinize the 130 iSchools’ GAI engagement. Consulting each iSchool’s website, we conducted a qualitative content analysis of their GAI policies. Eighty-seven (66.9%) have public-facing GAI policies; the other third do not. We teased out seven themes in these 87 iSchools’ policies: the ecology of GAI, opportunities and affordances, risk and concerns, conditions of use, best practices, compliance measures, and aspirations. We urge the iSchools to develop and implement a policy predicated on human-centered GAI literacy. This research ultimately represents both a call for self-reflexivity and a call to action.

Brenda Dervin made a tremendous contribution to both the fields of communication and information science through her Sense-Making Methodology. She was one of the first to advocate for a user-centered perspective in the field and had a tremendous impact on generations of researchers across various disciplines. Almost three years after her passing in December 2022, this panel brings together a diverse range of speakers to celebrate Dervin’s contribution to ASIS&T and to information science. This highly interactive panel will cover various topics ranging from interactions with Dervin to the use of Dervin’s SMM in theory, research, practice, human interaction, and artificial intelligence tools. The panel members have either engaged with Dervin the person, with her Sense-Making Methodology, or both. The session hopes to inspire the audience to use Dervin’s Sense-Making Methodology (SMM) in their research and practice.

As AI becomes increasingly integrated into the higher education and information landscape, its impact on libraries, education, and communication becomes more prevalent. Higher education and libraries are seeing AI integrations in the classroom, databases, and beyond, just waiting to make your work ‘just a little bit easier.’ Communication and literacy are under threat of mass simplification through AI summarization, drawing people farther apart from one another. At [my library’s name], we are taking steps to help educate ourselves and others about AI and Information Literacy. Libraries, as hubs of information, are the perfect place to pioneer this educational movement as we proceed into the age of AI.

Much research has focused on understanding digital literacy, its core competencies, and underlying inequalities, but much less is known about AI literacy. This study conducted a scoping review of papers written about AI literacy to 1) critically examine definitions, 2) discern approaches to study it, and 3) contribute to the ASIST theme “how information science research can be used to benefit society and to guide others.” Our results suggest that current frameworks focus on knowledge and skill acquisition, framing AI literacy as a bundle of competencies. Definitions of AI literacy are inconsistent, and most studies do not consider social factors. We argue that AI literacy frameworks need to expand to include critical engagement, informed decision-making, and resistance, taking an agency-driven approach. These insights can guide researchers, educators, and policymakers in fostering agency-centered AI literacy that empowers individuals in an increasingly AI-mediated world.

Performance artists, specifically theatre artists, are an under-researched community within library and information science (LIS). This study reviews the extant research on theatre performers’ information behavior, and ties these behaviors to theories within and outside of the LIS canon, specifically theories of information embodiment, body capital, and precarious work. This synthesis reveals that theatrical performance communities are embodying and encoding information behavior in unique ways that are of increasing interest for interdisciplinary LIS study. The information behavior of performance artists is affected by both their training as embodied and expressive workers, and their status as visible precarious laborers subjected to western ideals of appearance. New LIS research pathways and perspectives are elucidated via an examination of the information behavior within the performing arts.

The advancement of the ‘collections as data’ paradigm in libraries requires both library professionals and users of modern digital resources to acquire and use new skills such as data cleansing, visualization, use of Jupyter Notebooks, to name just a few. Introducing modules on datafication in libraries would contribute to the skills and knowledge of future library and information professionals. Continuous professional education courses also needs to address the demand for collection as data librarians. This poster presents emerging evidence on the ways higher and professional education respond to this shift in professional practice. We offer reflection from postgraduate modules from a Scottish University. We also explore the emerging trends in professional training on collections as data. We hope to stir a wider discussion and a fruitful dialogue on the educational aspects related to both postgraduate and professional education.

The rapid adoption of AI-generated content (AIGC) tools among China’s youth highlights a critical gap in understanding the literacy required to navigate this technological revolution. While existing research emphasizes technical and industrial implications, few studies address the multidimensional literacy frameworks needed for responsible AIGC engagement. This study investigates AIGC literacy by combining sentiment analysis of 4,000 Zhihu comments with semi-structured interviews involving 30 young participants. The approach uncovers public sentiment variations across topics, urban hierarchies, and tool releases, while qualitative data reveal key literacy dimensions, including technical proficiency, ethical awareness, and critical thinking. Findings indicate a nuanced sentiment landscape: 50.7% neutral, 27.3% positive, and 22.0% negative, with urban development and tool launches significantly influencing perceptions. The study contributes a foundational framework for measuring AIGC literacy, bridging theoretical and practical gaps. Implications extend to policymakers and educators, offering insights to design targeted interventions that foster responsible AIGC use among digitally native populations. By integrating empirical data with qualitative depth, this research advances global discourse on AI literacy in diverse sociocultural contexts.

Funding for nutrition research in the United States is less than 5% of the National Institutes of Health budget, so nutrition researchers often turn to published work. This provides an ideal environment for text mining, where entity detection is the task of finding food mentions in text and entity linking connects each food expression to a specific food. For example, the system should harmonize the expressions soybean, soy bean, soya bean and the scientific name Glycine max (L) Merrill along with their plural forms to a single concept soybean. However, the system must not harmonize soybean with soy sprouts because these different forms of soya foods have very different nutritional profiles. Despite the numerous food ontologies available, our work on developing a gold standard for food entities revealed a unique set of challenges that would limit the utility of automated extraction for nutrition researchers.

This study provides an in-depth examination of wind energy research, focusing on the United Nations Sustainable Development Goals (SDG). Using bibliometric techniques, the research analyzed published wind energy studies from 1996 to 2024, examining publication year, authorship, affiliation, source, country of origin, and contributions towards United Nation’s SDGs. The findings reveal a significant surge in annual research output, with a marked increase around 2018 and a dramatic acceleration in 2020. Research from 166 countries is represented, with China, the USA, and India leading the way in research output. The majority of publications fall under Energy Fuels and Green Sustainable Science Technology, with SDG 7 (Clean and Affordable Energy) being the primary focus.

During public crisis events, multimodal contents from social platforms such as text, images, and videos, contain valuable knowledge for official rapid response. However, the fragmentation of such knowledge across platforms undermines timely decision-making and limits the effectiveness of intelligent emergency response. This study proposes a cross-platform emergency knowledge collaboration method based on the multimodal heterogeneous information network. Firstly, the structure of the multimodal heterogeneous information network is defined and constructed for each specific platform, followed by corresponding visualizations. Then, the Enhanced-HGCN model with attention-based fusion is proposed to learn effective representations from the constructed networks. Based on the learned representations, the node–community collaboration strategy is designed to enable semantic and structural alignment across different platforms by linking similar nodes and their corresponding communities. Experimental results indicate that the constructed collaboration network achieves superior structural connectivity and richer semantic representation compared to single-platform networks. This collaboration network provides a stronger foundation for downstream tasks such as emergency knowledge recommendation.

Trust is essential for collective action in NGOs and grassroots organizations, yet Social Networking Platforms (SNPs) complicate these dynamics. Drawing on technological mediation theory, this study proposes a Technological Mediation of Trust (TMT) model to analyze how SNPs influence trust among communities, allies, and institutions. The model integrates psychological (subjective) and sociological (objective) dimensions of trust. Preliminary findings reveal a dual effect: while SNPs can enhance trust by showcasing local efforts, they predominantly hinder trust-building with bridging/linking capital networks and amplify distrust toward external entities. By exposing tensions between digital visibility and relational authenticity, the TMT model offers a framework to navigate trust risks in digitally mediated grassroots collaboration.

Articulating the need for an image can be challenging, as describing an image is more subjective than conveying textual information. This poster presents insight into the image-searching needs of visual artists and how they formulate queries. The data was collected through observations and interviews conducted with 19 visual artists engaged in their current creative tasks, which involve searching for images on social networking sites (SNSs). The findings revealed five types of image needs and five types of queries employed by visual artists. A unique query type identified on SNSs is the Ideation-oriented query, which explicitly asks for ideas in the search field for inspiration, direct use, or modification in their creative process. This poster highlights the artists’ needs in their natural settings and how they attempt to communicate with an AI-mediated environment to retrieve images using natural language.

r/datahoarder is a subreddit on the popular news aggregation and social media platform Reddit. Focused on individual and communal endeavors to gather and preserve a variety of digitized and born-digital data, r/datahoarder as a community coalesced around addressing perceived failures to save data across corporate, and governmental settings. While the impetus for engaging in datahoarding varies, this study examines key motivators for this work in a contemporary political context. This study shows that the key motivators expressed on r/datahoarder reveal new kinds of risk and approaches to address them that exist outside of digital settings, such as those denoted in the Digital Preservation Coalition’s Risk Framework,. The study concludes with implications for findings across a range of data preservation concerns including, but not limited to digital preservation best practices, data governance, and knowledge preservation within social media communities of practice.

As Artificial Intelligence (AI) advancements create new ethical issues and challenge existing legal regulations, traditional ethical principles and data ethics education often fail to reflect these nuances. This study explores how collaborative game design among students with diverse backgrounds fosters transformative learning in data ethics within a college course. It examines a conversation about motivations for enrolling in the course, pre-course reflections, and post-course reflections from 32 participants in a semester-long credit-based class. Thematic analysis reveals parallels with Mezirow’s transformative learning theory (1978) in four key phases of transformative learning. The preliminary findings demonstrate that students not only gained data ethics literacy but also internalized ethical thinking as part of their future goals. This poster highlights the potential of interdisciplinary and collaborative design-based pedagogy to cultivate digital ethical self-awareness and ethical judgements when navigating ethical issues in AI or machine learning (ML).

Privacy policies, as the most direct embodiment of the principle of informed consent, are facing a crisis of ineffectiveness. Users frequently face uncertainty about the collection, use, and potential consequences of their personal data. A Stressor–Cognitive Assessment–Coping Strategy framework was developed based on the Stress Coping Theory. We designed a login interface for a virtual Quantified Self (QS) APP and recruited 366 participants to conduct an experiment. Two-way ANOVA and SEM were used to analyze participants' behavioral responses and strategy preferences across different privacy notice types. The results reveal a double-edged sword effect of privacy notifications: while explicit notifications significantly reduce users' privacy uncertainty and enhance trust and comprehension of privacy policies, this transparency simultaneously heightens awareness of privacy risks.

This study explores how academic libraries can strategically partner with K–12 schools to provide professional development for educators. Drawing from surveys and interviews with academic library administrators and public school superintendents in New Jersey, findings reveal strong interest in collaborations focused on information literacy, educational technology, and project-based learning. The passage of New Jersey’s Information Literacy Law (S588/A4169) underscores the urgency for educator training, presenting academic librarians as valuable partners. The research highlights shared goals, barriers, and recommendations for building sustainable, standards-aligned partnerships.

It is critical to understand the Information Seeking Behavior of various social groups. Examining the ISB of painters, a group that is frequently disregarded in ISB literature, is the goal of this study. The researcher conducted in-depth interviews with ten professional painters. This study found that the information for artists categorizes into passive and active forms. Passive information serves as a creative trigger, while active information fills knowledge gaps during art creation process. Painters rely on their knowledge base but use traditional sources for problem solving. Understanding artists' information sources is crucial, with the inner world encompassing inspiration, senses, memories, and more, while the outer world includes social interactions, places, and objects. These dynamics shape the art creation process.

AI literacy is an emerging research topic, as various AI tools have become embedded in our everyday lives, particularly in the school context. The purposes of this exploratory study are twofold: 1) to investigate high school students’ AI literacy and 2) to explore their experiences using AI tools in the context of coursework. A mixed-methods approach was employed, combining a survey questionnaire with interviews and information world mapping visual-elicitation method. The findings indicate that students generally perceived themselves as having a basic understanding of AI and expressed confidence in using AI tools. However, many still relied on human sources, such as peers, to learn how to apply AI in coursework. Notably, gender differences in AI literacy were also observed. These preliminary findings highlight the need for formal AI education and course development in high schools, which may have implications for educators in designing future AI curricula.

Disabled and chronically ill populations experience significant barriers to navigating the healthcare system, including communication, attitudinal, and social barriers (CDC, 2025). Artificial intelligence (AI) may enable disabled and chronically ill individuals to mitigate these barriers. However, most literature exploring the use of AI in healthcare focuses on use by providers and institutions (Laranjo et al., 2016). There is a growing body of library and information science (LIS) research examining how disabled and chronically ill populations use technologies to manage their health and as tools for empowerment, information access, and communication support (Chen 2016; Costello & Murillo, 2014; Lundy, 2024; St. Jean, 2017). This poster reports developing doctoral student research investigating how disabled and chronically ill populations utilize AI-enabled chatbots as tools to navigate the healthcare system and manage their health. Semi-structured interviews are being conducted with a diverse sample of n=25 disabled and chronically ill participants. Guided by core principles of disability justice, we plan to conduct thematic analysis of the interview data. Our work aims to provide a critical understanding of the chatbot-facilitated information practices of disabled and chronically ill populations, and to contribute key design considerations for future technologies that support the health and well-being of these populations.

This study used Chatman's information poverty theory and conducted semi-structured interviews with 11 participants to explore information poverty that international students face in obtaining health information. The study identified four themes aligned with the theory: deception, risk-taking, secrecy, and situational relevance. Chatman’s six propositions suggest strategies to address these challenges. The results can inform universities and institutions in supporting international students’ well-being.

Artificial Intelligence (AI) rapidly expands in healthcare, yet little is known about how medical professionals perceive this transformation. We analyzed 7,136 tweets about AI posted by medical doctors between November 2022 and March 2025. Results show attitudes of medical doctors about AI are statistically different across the U.S. divisions (χ² (16) = 82.53, p <.001), with more favorable sentiment observed in the Pacific and New England divisions, and more negative sentiment in the South Atlantic division. Understanding these differences highlights geographic factors influencing AI acceptance, guiding responsible AI adoption in healthcare.

Harmful information spreads during conflict, causing significant problems due to the variation of forms it can take and ways it can spread. For humanitarian organizations operating in conflict zones, knowing how, when, and if to respond to harmful information while working to minimize further harm is a complex challenge. As demonstrated by a statement published by the International Committee of the Red Cross, civil society lacks the tools needed to address this challenge. This project responds to this statement by evaluating the efficacy of data governance tools. Results identify five tools humanitarian organizations can employ to unravel the complexity of each instance of informational spread, understanding that appropriate responses to these incidents can only be enacted after full explication. These tools are responsive to context and can be applied in both proactive and reactive instances. Thus, this project addresses a known lacuna and provides a pathway through which humanitarian organizations can workshop responses to the spread of harmful information within the varied contexts of conflict in which they work.

Recent policy changes and sweeping cuts to federal agencies in the United States (US) pose a significant threat to information scholars and practitioners in the US and elsewhere who benefit from the funding, services, programming, and support made possible by federal agencies like the Institute of Museum and Library Services (IMLS), National Institutes of Health (NIH), and the National Science Foundation (NSF). The termination of research grants, the deletion of public federal data sets, and mass layoffs across the federal sector in the US have left many early career scholars concerned about disruptions to their research and scholarly productivity. These disruptions have the potential to impact scholars around the world who, for example, rely on data that is under threat, or who collaborate with researchers based at institutions in the US. This panel will discuss future considerations for these actions' impact on information scholars, practitioners, and their communities. We will present strategies for fostering scholarly productivity through scholarly collaboration, data sharing and reuse, and information resilience. We aim to foster an open discussion with panelists and audience members to explore additional avenues and strategies that early career information scholars may pursue to navigate these challenges.

This alternative event is the inaugural convening of the AI Agnostics reading group. Using recently published texts from interdisciplinary fields, six participants will engage conference attendees in a rousing discussion of critical issues in artificial intelligence. Together, the reading group and conference attendees will unpack, reflect on, and wrestle with the myriad domains in which our lives are affected by AI and related ethical issues, policy concerns, and agency in use of non-use of AI in research and teaching. Using literature as a springboard, we will consider the costs of uncritical acceptance of artificial intelligence. We will gain insights into what information science scholars and practitioners can do to respond to the current moment for the good of society.

This study constructed an integrated framework for measuring LLMs’ knowledge breadth (able to cover multiple fields) and depth (able to handle complex problems) and perform calculations based on evidence. The current research evidence on measuring LLMs was obtained through a systematic review. Then, an integrated framework based on the matching of capability and task was constructed, where the generation capability and knowledge management task reflect the knowledge breadth, while other capabilities and tasks reflect the knowledge depth. Knowledge breadth and depth were measured through Coverage and Revealed Technological Advantage. The results showed that 36 matches of capability and task were selected from representative papers, and the knowledge breadth and depth panorama of LLMs was displayed. Furthermore, there is no absolute advantage or disadvantage in LLMs. This study clarifies the boundaries of the LLM’s measurement, and the framework further ensures the diversity of benchmark models and avoids redundancy and misalignment biases.

The field of scientific and technical information (STI) is characterized by limited open-source training data, strong timeliness requirements, and high demands for professional expertise, necessitating the combination of general capabilities and domain-specific capabilities of large language models (LLMs). Based on common STI research tasks, this study establishes benchmark datasets for evaluating LLMs in the STI field, comprising basic knowledge ability, dynamic research ability, and thematic research ability. A total of 1,557 objective and subjective questions were selected to evaluate the performance of eight LLMs developed by commercial organizations, research institutions, and universities in the STI field. The results indicate that LLMs perform well in terms of STI domain knowledge but still exhibit significant gaps in dynamic and thematic information research. There is a need to actively explore and promote the integration, adaptation, and application of LLM technologies in the STI field to provide robust support for high-quality and high-efficiency STI services.

This study investigates public perceptions of generative artificial intelligence (GenAI) in libraries through a large-scale analysis of X/Twitter posts. Using a mixed-method approach that combines temporal trend analysis, sentiment classification, and social network analysis, the researchers explore how discourse around GenAI and libraries has evolved over time, what emotional tones dominate the conversation, and which users or organizations act as key influencers. The findings reveal that discussions are predominantly negative in tone, with spikes driven by ethical and intellectual property concerns. Social network structures highlight both institutional authority and individual

bridge users, facilitating cross-domain engagement. The results contribute to the growing literature on GenAI in the library and GLAM (Galleries, Libraries, Archives, and Museums) sectors and offer a public-facing perspective on the opportunities and concerns emerging in real time.

The future of public library work is likely to feature emerging AI capabilities, which will have a significant impact on worker identity. The current study suggests that an analysis of the identity threats posed by a worker’s interactions with existing library technology have important implications for their future interactions with AI. This is because many of the contextual factors that are likely to moderate the impact of AI on workers have been present in several previous iterations of workplace technology. By analyzing audio diaries from 52 public library workers, the current study uncovers the existence of several identity threats introduced as workers interact with technology. The study also reveals the conditional factors that moderate technology’s impact on identity, as well as the strategies workers employ to respond to identity threats. By framing its implementation of future AI in light of these findings, the library profession will be in a better position to avoid previous mistakes.

The dissemination of fake news in the financial domain has surged in recent years. However, factors leading to this dissemination are less well studied. Drawing on Behavioral Reasoning Theory, this study investigates the factors influencing fake financial news sharing (FFNS) on social media, focusing on finance-specific variables such as herd behavior, financial literacy, and investment experience. Based on survey data from a sample of U.S. retail investors (n = 112), we employed Partial Least Squares (PLS) analysis to examine five key predictors of FFNS. The results indicate that perceived herd behavior and a sense of belonging are significant positive predictors. Although financial literacy and the act of authenticating news before sharing were hypothesized to reduce FFNS, their effects were statistically insignificant. These findings contribute to a deeper understanding of the psychological and contextual drivers of FFNS and offer practical insights for developing interventions to curb its spread.

Customer Engagement Management (CEM) is a customer-centric strategy focused on improving user experience, satisfaction, and long-term loyalty. This study explores the information needs within the CEM practices of a major international airline by conducting three sequential qualitative investigations involving key stakeholder groups: internal CEM team members, customers, and external travel agents. Through a triangulated analysis of interviews and survey data, the study reveals systemic information gaps and proposes design improvements for digital engagement strategies. Findings emphasize the significance of enhancing e-CEM platforms—such as websites and mobile applications—and integrating social-CEM approaches to improve real-time communication and engagement. The results also highlight how human-computer interaction (HCI) principles can support more effective information flows between organizations and stakeholders, ultimately strengthening customer engagement.

Japan has the largest ageing population worldwide, with increased numbers of people living with dementia. Dementia legislation aims to disseminate accurate information about dementia, starting with public libraries. However, the policies’ effectiveness has not been examined. Therefore, this study analysed how people obtain information about dementia and investigated the significance of disseminating such information in public libraries. An online survey was conducted with 516 people who had cared for someone living with dementia or mild cognitive impairment and who had searched for dementia information at least once in the past year. The respondents often searched online for dementia-related information, including symptoms and mechanisms. The reliability and accessibility of public libraries were lower than those of hospitals and administrative bodies, but the psychological barriers to obtaining information from public libraries were low. However, some respondents could not find materials about dementia in public libraries, suggesting a need for dementia support services.

The paper investigates how digital interventions can benefit the human development of deaf people in Bangladesh by considering the three pillars of sustainability - economic, social, and environmental. The study recognizes that while persons with disabilities, including deaf people, are an essential part of the development process, they often face social exclusion, particularly in low-resource settings like Bangladesh. Digital interventions such as sign language recognition systems and mobile applications have been developed to improve accessibility for the deaf community. However, most of these technologies have focused on sign language without addressing broader socio-economic and environmental factors. The research employs a qualitative approach through focus groups and stakeholder interviews to explore the needs, challenges, and capabilities of deaf people regarding digital interventions. Key findings suggest a strong need for health and education services, with a particular emphasis on improving digital connectivity. The study also highlights the barriers posed by digital inequality and the limited availability of sign language interpreters, recommending a more inclusive approach that integrates the sustainability pillars into the design of digital interventions. The research concludes that collaboration with the government and stakeholders is crucial for creating sustainable, scalable solutions for the deaf community in Bangladesh.

Tertiary students are a widely studied group within the information behavior literature. This is in part because they are quite a diverse group of people but through a shared context of university, have overlaps in information environments. However, studies tend to consider the entirety of tertiary students, or only migrant students, rather than comparing between students that have migrated for study versus those that have not (sedentary students). The distinction is important as migrant students draw on transnational information sources, which further enriches their information experiences. Further, there is a tendency to emphasize the vulnerability of migrant students’ experiences rather than appreciating their strengths. To address these gaps, we ran a made-for-purpose survey (N=202). While there were similarities in information seeking behaviors, we found statistically significant differences for migrant students: they were more likely to interact with perceived disagreeable and perceived useful information, and to curate perceived useful information. This research expands on existing work by showcasing quantitative differences in information behaviors between migrant and sedentary student groups. This paper contributes to the information behavior literature by providing a different perspective on migrant students’ information behaviors which can be seen as strengths, rather than weaknesses.

The proposed presentation develops a matter that is at the heart of the History and Foundations of Information Science Special Interest Group (HFIS-SIG)—memory. The introduction details conceptions of memory, as set forth by some prominent thinkers. That is, the past remains for all to perceive and to build upon. It also includes the essential component of knowledge; knowledge of what has gone before is (and should be) a part of our knowledge of contemporary time. The session is divided into three sections: Lin Wang will speak to the importance of historical knowledge, particularly from a Chinese perspective. Anne Gilliland addresses the role of archives and archival praxis as they foster the retention of memory; she will ask some important questions related to archives and memory. Wayne de Fremery turns attention to bibliography as a vital aspect of memory. All presentations will address the impact of generative AI on the practices of historical knowledge

This study evaluates the usability of four metaverse libraries—Chilgok Public Library, Daegu Integrated Public Library, Community Virtual Library, and Caledon Library—using a multi-method approach. A task-based evaluation based on Nielsen’s usability criteria was combined with the System Usability Scale (SUS), AttrakDiff, and Photovoice to capture users’ cognitive, behavioral, and emotional experiences. Results show that unfamiliarity with virtual environments posed initial challenges, particularly in Second Life-based libraries. However, users’ efficiency improved with repeated tasks, and participants without prior metaverse experience reported higher emotional satisfaction. Photovoice data revealed immersive visuals alongside usability issues such as navigation difficulties and unclear interactions. The study highlights the need for platform-specific design improvements and usability training. It contributes to metaverse library research by offering comprehensive evaluation methods and suggesting directions for designing more user-centered metaverse library experiences.

The purpose of this study is to explore users' expectations of LLMs, examine their confirmation of perceived system performance, and examine how these factors influence their overall satisfaction with the system. We analyzed data collected from LLM users through an online survey using Welch’s ANOVA and regression analysis. The findings demonstrate that users’ expectations and confirmation of LLMs are fluid across different socio-cultural variables, spanning age, gender, and educational levels. Additionally, users’ perceived system performance, of LLMS, significantly influences their confirmation of the system. Specifically, both perceived usefulness and perceived ease of use have a statistically significant effect on confirmation. Both of our sub-models demonstrate that perceived system performance influences users' confirmation of a given system, and users’ confirmation is a strong determinant of their satisfaction. Furthermore, our results indicate an uneven distribution and penetration of AI technologies with respect to age, gender and educational level.

This study explores how the National Central Library in Taiwan leverages a big data service platform to empower public libraries, enabling them to enhance reader participation through data-driven decisions. The platform systematically processes de-identified circulation data through comprehensive cleaning, integration, and standardization procedures, enabling libraries to gain actionable insights through advanced visualization tools. This data empowerment initiative reflects the evolution of Taiwan's libraries towards Library 4.0, where analytics capabilities enable libraries to transform from passive information providers to active service innovators, providing personalized, differentiated reader experiences. The platform strengthens public libraries' capacity to promote reading engagement by providing detailed metrics and trend reports, facilitating evidence-based policy making and service improvements. Through continuous data analysis and visualization, libraries can better understand their communities' reading preferences and adapt their services accordingly. In addition to basic statistical analysis, the platform incorporates and develops data mining techniques to analyze reading behaviors across demographics, offering deeper insights into reader interests and preferences. Furthermore, the study investigates the application of these data mining techniques to facilitate reader resource recommendations, ultimately enhancing library services and promoting a more engaged reading community.

The open source of DeepSeek has enabled the application of artificial intelligence generated content (AIGC) to enter a rapid development stage. However, it has also increasingly highlighted many problems. This study delves into the specific problems of AIGC, constructs a collaborative governance mechanism from three aspects: goal collaboration, process collaboration, and inter-subject collaboration, and provides an implementation path for multi-subject collaborative governance. The study found that in the DeepSeek era, AIGC technology faces problems such as user privacy leakage, insufficient content quality assessment, and intellectual property and ethical conflicts during its application. This paper emphasizes that establishing a collaborative governance mechanism is a key way to deal with these problems in the long term. The government, industry, platform, and users should participate together to strengthen industry supervision, improve the self-discipline review mechanism, and enhance AI literacy education, so as to jointly promote the long-term stability and healthy development of the artificial intelligence industry. This study is of great significance to ensuring information security and promoting the healthy development of the artificial intelligence industry.

Government agencies' use of AI has the capacity to improve and radically transform the essence of digital government. It is critical to avoid restrictive policies that constrain innovation or, conversely, insufficient regulation that could lead to social and ethical issues. However, the rapid deployment of AI in government has faced criticism for posing significant challenges to democracy, especially given the inherent governance issues. It is critical as it encompasses responsible and effective use to protect democratic values in government agencies’ structures, processes, and practices. This study contributes to information science by exploring how federal agencies, in their compliance plans, are responding to the Management and Budget memorandum requirement for advancing AI governance, innovation, and risk management through a content analysis of 23 federal agencies’ compliance plans. The conclusion indicated that government agencies predominantly advance AI governance, innovation, and risk management by focusing on control rather than transformative innovation.

Peer review constitutes a fundamental part of the global system of scholarly communication. Generative Artificial Intelligence (GenAI) poses an existential challenge to this system. is the first empirical study to scrutinize the intersection of AI and peer review from the perspective of information and library scientists. It is also the first to discuss core information practices, namely use and avoidance, not only in the context of peer review, but in the context of AI more broadly. Our survey participants addressed their personal use or avoidance of AI, their overall stance on AI use or avoidance, detecting and sanctioning illicit AI use, starting to use or continuing to avoid AI, developing an AI use policy, and what they perceived as the future (both predicted and hoped-for) of AI. Most respondents underscored the indubitably human-centered nature of the peer review process. They gave their imprimatur only to the most limited uses of AI, e.g. for activities such as checking grammar and style. Their AI avoidance took root in deeply felt moral and ethical commitments as well as more prosaic concerns about bias and quality. We discuss the implications of these findings for research and practice.

We examined qualitative methods reporting in information science research by analyzing ASIS&T conference papers (2018-2022) and comparing findings with journal publishing guidelines. Our study of 117 papers using exclusively qualitative methods revealed significant gaps in methodological documentation. While 78.6% of papers involved human subjects research (primarily interviews), only 28.3% mentioned IRB approval. Similarly, 66.7% failed to describe analytical tools used. Journal publishing guidelines across the field showed inconsistent requirements for qualitative research reporting, with some mandating IRB disclosure while others provided minimal direction. The prevalent use of passive voice in methods sections often obscured critical information about data producers and collection processes. These findings demonstrate a need for more standardized reporting guidelines for qualitative research in information science. We recommend that ASIS&T publishing venues require authors to provide, at minimum: data production year(s), clear identification of data producers, persistent identifiers when available, and IRB approval status for human subjects research. These measures could enhance transparency and facilitate better understanding of qualitative research practices in the field.

As digital infrastructures and methods increasingly shape how cultural memory is preserved, accessed, and interpreted, questions of collaboration and participation have become central to both research and pedagogical practices in digital humanities and cultural heritage contexts. This panel explores the promises and challenges of participatory approaches to digital humanities and cultural heritage work. Bringing together five speakers in various domains of digital humanities, community archives, and digital curation, this panel offers multiple perspectives on how to engage different communities of interest, such as students, interdisciplinary scholars, librarians and practitioners, as well as local communities, in participatory digital humanities and cultural heritage work. Following the individual presentations, panelists will facilitate open discussions with attendees, seeking to collectively explore questions including how to design participatory work in digital humanities and cultural heritage practices, how to engage communities and collaborators in participatory work, and how to address the challenges that emerge in participatory processes. Through this collaborative and interactive approach, this panel seeks to advance knowledge production practices of digital humanities and cultural heritage, advocating for a “participatory future” of digital humanities and cultural heritage work.