Prove It. Show It: A Framework for Validating Learning and Making AI Use Transparent

AI has disrupted a core assumption in higher education: that student work reliably demonstrates student learning. As AI tools generate high-quality outputs, traditional assessments no longer provide clear evidence of thinking, while expectations for AI use remain inconsistent and often unclear to students. This session introduces a structured, faculty-ready framework built on a decision-based process that helps faculty determine whether an assessment still produces valid evidence of learning and clearly defines the conditions under which that learning must occur. Participants will examine how this process identifies required evidence, detects where AI can meaningfully complete or shortcut learning, and guides targeted redesign to restore observable thinking. At the same time, the framework requires faculty to make AI expectations explicit, ensuring students understand how AI can and cannot be used within each assessment. This integrated approach ensures that learning is both valid (can be trusted) and visible (clearly communicated). Developed through multiphase faculty implementation, this model moves beyond assignment redesign to establish a scalable system for validating learning and clarifying expectations. Attendees will leave with a repeatable process they can apply immediately to strengthen academic integrity, improve student clarity, and align assessment practices with AI-enabled learning environments.