2025/03/11 by Ko Chun Ru, Rong Tang · 1 voice · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Genetics, Bioinformatics, and Biomedical Research #Online Learning and Analytics #Teaching and Learning Programming
paper · pdf · doi:10.47989/ir30iconf47182
openalex publication_date 2025/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Introduction. As the integration of artificial intelligence (AI) rapidly advances, academic libraries are increasingly pivotal in supporting AI literacy among students and faculty. Method. Through content analysis, the present study examines 70 newly developed generative AI LibGuides from academic libraries affiliated with the association of research libraries (ARL) and the Oberlin group, using the EDUCAUSE AI literacy framework. Analysis. Through a detailed examination, the present research reorganizes and improves the EDUCAUSE AI literacy framework, proposing a more comprehensive version tailored to higher education needs. The adapted framework fills the gaps in the original model and offers a nuanced approach to AI literacy, reflecting the unique challenges faced by academic libraries. Results. The findings reveal that most LibGuides emphasize foundational AI tools and responsible use, with less focus on advanced technical competencies related to AI creation. Significant differences were observed between ARL and Oberlin Group LibGuides, with ARL offering more comprehensive coverage. To address these differences, consistent training and knowledge sharing initiatives are recommended to ensure a common standard of AI literacy support across academic libraries. Conclusion. This study provides insights into the role of libraries in promoting generative AI literacy and identifies areas for future strategic partnerships and improvement.