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

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, 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.

Digital literacy is well-studied across disciplines, with established attention to core competencies and social inequalities. However, artificial intelligence (AI) literacy remains underexplored. To address this gap, we conducted a scoping review on AI literacy to: (1) consolidate current definitions and pinpoint conceptual gaps, (2) evaluate methodological approaches and their relevance in practice, and (3) examine how social inequalities are considered in AI literacy studies. Definitions of AI literacy are inconsistent across and within disciplines, and most studies do not consider social factors. Most definitions focus on knowledge and skill acquisition, framing AI literacy as a suite of acquired competencies. We argue that current understandings of AI literacy need to expand to include informed decision-making, critical engagement, and resistance to technological coercion by taking an agency-driven approach. These insights can guide researchers, educators, and policymakers in fostering an 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 ties the extant research on theatre performers’ information behavior to theories within and outside of the LIS canon, specifically theories of information embodiment, body capital, and precarious work. These theories were selected as analytical frames due to their critical relevance and suitability due to the emotive and physical nature of performance. This initial investigation concludes and contends 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 reveals significant sentiment variations across topics, urban hierarchies, and tool releases, offering a layered view of how public perceptions evolve in response to contextual and technological shifts. Meanwhile, qualitative data uncover 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 presents a bibliometric analysis of wind energy research from 1996 to 2024, examining its contributions to the United Nation's Sustainable Development Goals. The analysis reveals a significant surge in research output, particularly after 2018 and accelerating in 2020. With global representation from 166 countries, China, the USA, and India lead the way. The findings show that wind energy research primarily aligns with SDG 7, focusing on clean and affordable energy. By visualizing the intellectual landscape and identifying key themes and trends, this study aims to provide a comprehensive understanding of wind energy research and illuminate research hotspots.

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.

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.

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.

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 study investigates professional designers' integration of AI tools within workflows across five key stages: Discovery, Ideation, Development, Refinement, and Finalization. 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 (such as ideation, content generation, refinement), while interpretive tasks requiring contextual judgment remain under human control. Designers viewed AI as an assistive co-creator accelerating iteration and automating labor, rather than replacing core creativity. Offloading procedural tasks and early ideation to AI enabled deeper focus on conceptual and critical decisions. Designers used 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.

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.

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.

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.

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—multimodal content generation, creative output, and interactive iteration—can be harnessed to address the current limitations of Nüshu narrative models. Drawing on interdisciplinary literature, this study examines the technical attributes of generative AI and its interplay with mechanisms of interactive digital storytelling. Based on the SPP (System–Process–Product) model, 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. 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 interactive 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.