The conversation about AI in higher education is loud, anxious, and often dominated by the largest institutions with the most resources to navigate it. Small liberal arts colleges are watching from a complicated position: wary of the hype (if not downright resistant), uncertain about the risks, and not entirely sure the opportunity being described is one that was designed with them in mind. This presentation will argue that, for small colleges with distinctive curricula, signature programs, and their own institutional culture, AI can offers something more interesting than automation or efficiency gains. It offers, for the first time, the ability to build infrastructure that actually reflects what makes them distinctive. Tools that understand your academic model. Dashboards that speak your institutional language. Systems that serve your students, and not a generalized approximation of them.New College of Florida decided to rethink our approach to software built for someone else and start building tools that are genuinely, specifically ours. In a single academic year, a small cross-functional team (IT, Institutional Research, Registrar, Provost's Office, and students) built working dashboards for advising, athlete eligibility, thesis progress, early alert and retention, and faculty workloads, at a fraction of commercial licensing costs.This session is a practitioner's honest account of how we did it, and why the real payoff isn't efficiency. It's institutional identity.
Higher education is entering a new era where AI is reshaping how institutions engage students, support staff, and operate at scale. Yet many campuses are still struggling to determine what true AI readiness actually looks like.This session explores how institutions can move beyond experimentation to build an AI-ready campus grounded in strategy, governance, and practical implementation. Attendees will learn how colleges are using AI to improve student engagement, streamline operations, and expand institutional capacity while maintaining a human-centered student experience.Through real examples and an interactive readiness framework, participants will examine common barriers to adoption, assess their institution’s current level of AI readiness, and identify practical next steps for moving forward. The session will also explore how AI agents and AI-supported workflows are changing expectations around responsiveness, personalization, and operational scalability across higher education.
What can AI do, and what should it do? My approach to AI and Ethics builds a single inquiry from two disciplines that rarely share a room: computer science, where students implement a neural network from first principles without libraries, and serious philosophy, where they reason about how one ought to act. The pairing exposes a tension at the heart of the liberal arts in the age of AI. If a machine ties your shoes, do you ever learn to tie them yourself—or to do your arithmetic, your writing, your deciding? The liberal arts exist to cultivate precisely the capacities AI now offers to outsource: judgment, reflection, moral agency, the power to be more than a child of one’s time. This talk asks whether teaching AI must mean defending those capacities, and how one course might do both at once.
Large language models (LLM) are being used in educational settings to provide guidance, feedback, and policy-related judgments. However, these systems produce inconsistent responses to similar cases, raising concerns about fairness, transparency, and trust. Rather than relying on probabilistic model output, our approach represents cases in terms of salient features and applies principles derived from institutional academic policies to guide decisions. The LLM remains responsible for interpreting cases, while a separate layer using principles ensures similar cases are treated consistently. This approach offers a middle ground between unrestricted generative AI and rule-based systems. By making the basis for decisions explicit and inspectable, it provides greater transparency, supports accountability,and allows policies to be operationalized in a consistent form. We illustrate the approach using examples drawn from our college policies concerning student use of AI and discuss the implications for responsible deployment within the liberal arts.
Title:The AI-Native Classroom: Early Lessons from a Carnegie Mellon PilotAbstract:For the past year, debates about AI in higher education have largely focused on student access to tools such as ChatGPT. Yet access to AI and learning in an AI-native environment are not the same thing. This session examines what happened when AI was integrated directly into course materials, learning activities, and student study workflows in a graduate-level Carnegie Mellon course. Drawing on observations, usage data, student feedback, and faculty reflections, the presentation explores how students actually engage with AI when it becomes part of the learning environment itself. Participants will discuss implications for pedagogy, academic integrity, faculty roles, and the future of liberal arts education in an increasingly AI-mediated world.
Artificial intelligence presents liberal arts educators with a profound contradiction: AI can automate portions of research, analysis, and writing while simultaneously creating new opportunities for inquiry, interdisciplinary thinking, and human-centered learning. This interactive session explores how liberal arts classrooms can utilize AI not to replace student thought, but to deepen interpretation, synthesis, ethical reasoning, and creativity. Moving beyond routine comprehension, this presentation demonstrates how educators can leverage AI as an analytical partner to heighten critical thinking, expand explorative skills, and preserve student intellectual agency. Participants will engage with interdisciplinary classroom activities that combine history, sociology, philosophy, geography, economics, and the arts. Special attention will be given to pedagogical models that emphasize critical consciousness, adaptability, and the synthesis of complex data during periods of technological change. Attendees will leave with practical, scalable frameworks that empower students to bridge the gap between the synthetic capabilities of AI and the sacred, original human capacity for insight, interpretation, and meaning-making.
