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, 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.