MiALA 2026 Annual Conference

Academic librarians support course materials through OER, course guides, and reserves. This lightning talk shares a case study in which a newly hired instructor partnered with library staff to create a zero-cost, multimedia set of course resources. By combining open textbooks, library ebooks, podcasts, and expert video content, the collaboration supported Universal Design for Learning, broadened perspectives, and improved access while reducing student costs.

Learning Objective(s): 1) Describe the pedagogical value of incorporating podcasts and video alongside traditional course readings; 2) Explain how multimodal resources support Universal Design for Learning and undergraduate engagement; and 3) Apply a scale-able approach for expanding course materials beyond a single-textbook model while reducing student costs.

This lightning talk introduces the AI Literacy Compass—a values-first, harm-aware framework for working with AI instruction as a librarian. Beginning at True North (learning, equity, integrity, environment, labor), then moving through literacy, boundaries, and practice, the session addresses complex librarian and campus attitudes, from principled refusal to uncritical, shortcut-focused use. Attendees will leave with language and decision tools to support critical, ethically grounded engagement in their own AI-related work.

Learning Objective(s): Articulate a values-first definition of AI literacy that supports both critical use and principled refusal of generative AI tools

The use of AI tools in evidence synthesis is expanding rapidly, and librarians are frequently asked by researchers for guidance on selecting the best AI tools to enhance their work. This session will introduce some of the available AI tools and where they fit in the evidence synthesis process, resources for critically appraising their use, and the possibilities and potential pitfalls of integrating AI tools.

Learning Objective(s): 1) Participants will identify how artificial intelligence could be used in evidence synthesis; 2) Participants will evaluate AI tools for use in evidence synthesis to determine effectiveness and use in their own research; and 3) Participants will critically appraise AI tools for use in evidence synthesis.