Teaching and Learning with AI Conference

This session explores how the rapid adoption of AI in teaching, research support, and integrity workflows is outpacing the development of policy, privacy, and equity safeguards. Participants get a concise ethics update on bias, opacity, hallucinations, detector limits, and data governance risks, then apply a red–yellow–green framework to classify AI uses, set guardrails, and identify prohibited practices. The session concludes with a straightforward implementation plan that incorporates stakeholder input, vetted tools, and update cycles aligned with learning and equity outcomes. Attendees leave with a campus-ready traffic-light scaffold, a one-page checklist for privacy and transparency, and a pilot template for staged rollout.#AIEthics #Policy #AcademicIntegrity