Teaching and Learning with AI Conference

Despite widespread student use of generative AI, higher education lacks shared frameworks or coordinated approaches for teaching AI literacy, leaving instructional responsibility fragmented across institutions and roles. Academic librarians have taken on a growing role in supporting students’ understanding of generative AI through AI literacy interventions. This session presents findings from an environmental scan and textual analysis of library-led AI educational resources to examine how libraries respond to the challenges of generative AI in practice. Participants will explore emerging best practices, existing gaps in AI literacy curricula, and opportunities to adapt these approaches in their own institutional contexts. #AILiteracy #LibraryInstruction #HigherEducation

Faculty often approach artificial intelligence with caution due to concerns about the unknown, academic integrity, ethics, and instructional control. This session highlights how faculty overcame AI hesitancy and strategically implemented LOAH (Learning Objective and Assessment Helper), ChatGPT, and Grammarly to strengthen course alignment, enhance critical thinking, and improve assessment design in a Health Administration Capstone course. Participants will explore practical workflows for generating learning objectives, designing aligned assessments, refining instructional materials, and increasing student engagement while maintaining pedagogical rigor, transparency, and ethical AI use. A stepwise approach to creating high-impact weekly modules aligned with Quality Matters principles will also be shared.

As Artificial Intelligence evolves from a novelty to an essential literacy, institutional leaders face a critical strategic inflection point: how to move beyond reactive policy-making toward proactive, scalable support. This session argues that the most effective way to foster AI fluency is not through digital modules alone, but through the strategic implementation of physical "third spaces." By examining a prestigious collaborative network—including the University of Minnesota’s AI Makerspace, Stanford’s Tinkery, and Notre Dame’s Lab for AI (LAITL)—we demonstrate how physical hubs serve as the bridge between abstract institutional strategy and classroom innovation.We will deconstruct the leadership logistics required to sustain these environments, specifically focusing on interdisciplinary staffing models, cross-departmental funding structures, and the curation of "low-stakes" hardware/software ecosystems. We shift the narrative from AI as a departmental silo to AI as a centralized library or lab resource that democratizes access. Participants will gain a high-level roadmap to transition their institutional strategy from a "policy-first" defensive posture to a "play-first" innovation culture, ultimately empowering faculty and students to move from passive consumers to ethical creators.