From the ancient Greeks forward, liberal arts education has never been only about storing information. It has been about forming judgment through practice: rhetoric in the agora, philosophical dialogue, ethical deliberation, and the disciplined use of language in public life. Oratory, dialectic, and disputation were not simply ways of displaying knowledge, but of testing whether a person could think, respond, persuade, and act wisely in context. As generative AI unsettles traditional assessments, this older tradition becomes newly relevant. This presentation argues that AI-powered simulations can recover something essential to liberal arts learning by shifting assessment from static recall to situated performance. Using GENESIS as a case example, this talk will explore how conversational, competency-based simulations can make visible the capacities the liberal arts have long aimed to cultivate: judgment, communication, ethical reasoning, interpretive agility, and the ability to use language as action. Rather than abandoning the humanistic tradition, simulations may help us reclaim it in a new form.
Vibe coding — building working software through natural-language conversation with AI rather than traditional programming — has quietly removed the technical barrier that once kept faculty and students from creating their own interactive learning tools. This session embodies the symposium's central paradox: the same capability that lets a student generate code without understanding it can let a humanities professor build a simulation that finally makes an abstract concept click. In thirty minutes, participants watch a fully playable classroom simulation emerge live from a plain-language prompt, then interrogate what that means for learning. When a literature, economics, or government instructor can prototype a simulation in minutes, who is doing the thinking — and is that a problem or a breakthrough? Participants leave with a shared, honest framework for deciding when vibe-coded simulations deepen understanding and when they short-circuit it.
What is a faculty learning community (FLC)? Why is this model so relevant for the topic of AI and the Liberal Arts? This session proposes to answer these questions by introducing the FLC model, which brings together faculty and staff in a community of practice, and to argue for its relevance as a means of fostering interdisciplinary and collaborative connections, debates, and discussions, as well as practical outputs, in relation to the role of AI in higher education and more specifically in the Liberal Arts. Through a demonstration of how to form an AI-focused FLC, interactive activities, and shared resources, this session will leave the audience with the means of forming their own AI-focused FLCs at their home institutions, as well as giving audience members the opportunity to connect with colleagues from a variety of institutions who plan to undertake this same goal.
Visiting Assistant Professor - Francophone Studies, Trinity College
Elisabeth Herbst Buzay is a Visiting Assistant Professor of Francophone Studies at Trinity College. Her research focuses on topics including multimedia medievalism and extraordinary communication, in works ranging from French Medieval literature to contemporary French fantasy works... Read More →
Saturday October 17, 2026 3:30pm - 4:00pm EDT Blaustein 210
When AI arrived on campus, most institutions responded the same way: individual faculty experimenting in isolation, policy committees deliberating, and a general sense of productive chaos. At Hobart and William Smith Colleges, the Digital Learning Center took a different path: restructuring one role to make AI integration its central focus, then building outward from there. In this interactive session, participants will explore how a single department can catalyze institution-wide AI culture change without a massive budget or a dedicated AI office. Drawing on initiatives including a faculty Community of Practice, a BoodleBox pilot, and intentional strategies for amplifying student voice, this session offers a replicable framework for moving from scattered experimentation to coordinated community effort. Participants will leave with concrete strategies they can adapt to their own institutional contexts and the conviction that meaningful, human-centered AI integration is within reach, regardless of institutional size or resources.
