Artificial intelligence adoption in higher education is often discussed through policy or technology strategy, but much of the real work is happening informally through experimentation, peer learning, and day-to-day problem solving across institutions. This session explores how AI Communities of Practice can help universities move from fragmented experimentation toward more thoughtful and coordinated approaches to AI governance. Drawing from experiences supporting AI initiatives in higher education, the session examines how faculty, staff, and administrators make decisions about AI use while institutional guidance and norms are still evolving. The conversation will focus on professional judgment, trust, ethical uncertainty, institutional culture, and the hidden labor involved in helping colleagues make sense of rapidly changing technologies. Rather than viewing governance only as a top-down policy process, this session considers how governance also develops socially through conversation, collaboration, and shared institutional learning.
As many campuses begin adding AI tools into their classrooms, there is immense pressure to figure out how to quickly implements these technologies in order to avoid being left behind. As digital humanists, though, we also emphasize incorporating new technology carefully and critically which often contrasts with the ways these technologies flood bland, AI generated text into our classrooms. To this end, the presenter will offer his approach and lessons learned from implementing an assignment sequence that required students to “audit” the outputs of generative AI platforms. Students worked in small groups to develop a report that assessed the accuracy and effectiveness of the genAI platforms’ source use. In treating AI technologies as a site for critical inquiry, students acquired a more nuanced understanding of AI hallucinations and a greater appreciation for how academic sources support arguments.
I build the AI systems that liberal arts educators worry about. Seven years of designing enterprise cloud and AI platforms for higher education and healthcare has produced an unexpected conviction: the more capable these systems become, the more indispensable the liberal arts skills they cannot replicate. This session names the paradox directly. The same tools that automate writing also raise the bar on judgment about what should be written. The same models that summarize at scale make source criticism a survival skill, not a humanities luxury. Drawing on three production case studies advising, hiring, and public communication .I will trace where AI fails, why it fails in those specific places, and how that maps onto the disciplinary strengths of a liberal arts curriculum. Participants leave with a shared vocabulary for talking to students and employers about the work AI cannot do and the work it makes more valuable.
Academic Integrity in the Age of AI (Bertram Gallant, Davis, & Khan, 2026) includes a speculative fiction story imagining a university in 2045, a future in which institutions layer AI onto outdated teaching and assessment models instead of rethinking academic integrity for this new era. This pop-up book club uses that story to spark conversation across two 30-minute sessions. No advance reading required - we'll hand out short quotes to react to on the spot. In the first session, we'll dig into where our own institutions already feel like they're drifting toward that imagined future. In the second session, we turn toward alternatives: what could a better path actually look like? Each session stands on its own, so come to one or both.
