A Learning-Centered Undergraduate AI Policy: Translating a Taxonomy of Generative AI Use into Classroom Practice

As generative AI becomes embedded in undergraduate coursework, instructors face the challenge of moving beyond prohibition and detection toward guidance that supports learning. This policy presents a student-facing framework for AI use grounded in an empirically derived taxonomy of generative AI engagement in higher education. Rather than framing AI use as simply allowed or forbidden, the policy organizes AI interactions along a continuum—from avoidance and task escape to momentum-building, feedback-seeking, conceptual learning, and magnification of student work. Each category is paired with clear expectations (prohibited, discouraged, allowed, encouraged) and documentation requirements. The policy aims to cultivate student judgment about when AI supports productive struggle and disciplinary growth versus when it primarily accelerates task completion. By embedding research into classroom-level governance, this document models policy as pedagogy—supporting ethical, transparent, and learning-centered AI integration in 2026 and beyond.