Developing Course-Level AI Use Policies in Higher Education: From Principles to Practice

The rapid diffusion of generative artificial intelligence in higher education has exposed a persistent gap between institutional AI governance frameworks and the day-to-day realities of classroom practice. While universities and international organizations have articulated ethical principles, governance requirements, and high-level guidance for AI use, faculty often lack concrete support for translating these frameworks into actionable expectations within individual courses. As a result, instructors face growing uncertainty around assessment design, academic integrity, and responsible AI integration at the course level.

This paper addresses that gap by examining course-level AI use policies as a critical yet underdeveloped instrument of educational governance. Drawing on research from ethics, governance studies, learning sciences, and assessment theory, the paper synthesizes key arguments for why explicit course-level policies are essential for protecting learning integrity, maintaining student agency, and supporting equitable access in AI-shaped academic environments. It further analyzes emerging policy guidance from international organizations and U.S. state-level frameworks to identify practical governance strategies that can inform higher education practice.

Building on this foundation, the paper proposes a set of design principles for developing effective course-level AI policies, emphasizing collaborative development, institutional alignment, pedagogical coherence, transparency, ethical modeling, accountability, and adaptability. To illustrate how these principles can be operationalized, the paper presents an illustrative sample course-level AI use policy that faculty can adapt to disciplinary and institutional contexts.