Students thrive when they encounter faculty with varied perspectives on AI—some who integrate it deeply, others who design AI-resilient assignments. Drawing on Connecticut College's AI@Conn Initiative, this session explores how navigating these differences cultivates metacognitive flexibility, adaptive expertise, and intellectual agency. Rather than seeking institutional uniformity, we argue that pedagogical diversity prepares students to work across professional contexts where AI adoption varies widely. Participants will gain strategies for supporting faculty across the AI spectrum and tools for helping students understand the reasoning behind different approaches.
VP for Information Services & Librarian of the College, Connecticut College
As vice president for Information Services and librarian of the College at Connecticut College, Gardzina is the chief information officer and leads both the libraries and Information Technology of the College, including traditional library services, administrative computing, telecommunications... Read More →
Saturday June 13, 2026 9:00am - 9:30am EDT Suwannee 2
Abstract: This session will present two multifaceted assignments and corresponding activities that use AI tools for critical thinking and the writing process. The activities will focus on invention, research, and revision. The activities draw on DEER praxis which emphasizes, “defined engagements with AI tools for specific purposes, and generous use of reflection” (Cummings et al., p. 1). The speakers will demonstrate how AI tools can provide a foundational background and understanding of a topic so that students can apply this knowledge in complex and creative ways to assignments. #AIactivities, #AIreflection, #layeredlearningCummings, R., et al. (2024). Generative AI in first-year writing: An early analysis of affordances,limitations, and a framework for the future. Computers and Composition, 71, 102827.https://doi.org/10.1016/j.compcom.2024.102827.
As AI systems generate fluent answers instantly, traditional assessments struggle to distinguish performance from understanding. This session introduces a judgment-centered teaching framework that uses AI as productive friction rather than a shortcut. Participants will explore how deliberately designed prompts, contradictions, and AI-generated confidence can expose reasoning, surface misconceptions, and support deeper learning without relying on surveillance or detection tools. Practical classroom examples from government, history, and composition courses will illustrate how authority can be exercised through question design rather than answer control.
