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

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.