Institutional AI Governance in Higher Education: A Comparative Policy Analysis and Proposed Governance Framework

Abstract

The rapid adoption of artificial intelligence (AI) in higher education has created new opportunities for teaching, learning, research, and institutional administration while raising important governance challenges related to ethics, academic integrity, transparency, accountability, and responsible AI use. As universities increasingly develop institutional guidance and policies to regulate AI technologies, there is a growing need to understand how these governance approaches align with internationally recognized principles and differ across institutions. This study examines institutional AI governance in higher education through a qualitative comparative policy analysis of official AI governance documents published by the American University of Beirut (AUB), Lebanese American University (LAU), Harvard University, and Stanford University. The study adopted a qualitative document analysis design and employed a comparative policy analysis approach. To enhance transparency in document selection, the study adapted the PRISMA 2020 framework to identify, screen, and select official institutional AI governance documents. Eight governance documents were analyzed using deductive thematic analysis based on ten governance dimensions derived from the UNESCO Recommendation on the Ethics of Artificial Intelligence (2021), the UNESCO Guidance for Generative AI in Education and Research (2023), and the OECD AI Principles. The findings indicate that all four universities emphasize responsible AI governance through principles of transparency, accountability, academic integrity, ethical AI use, privacy and data governance, human oversight, AI literacy, assessment, research governance, and institutional support. While Harvard and Stanford provide more comprehensive implementation guidance, AUB and LAU demonstrate emerging governance approaches that reflect their institutional contexts. Despite differences in governance maturity and documentation structure, the universities share a commitment to integrating AI responsibly while preserving academic values and institutional integrity. The study concludes by proposing an Institutional AI Governance Framework for Higher Education that synthesizes the comparative findings and offers practical guidance for universities seeking to strengthen AI governance. The findings contribute to the growing body of knowledge on institutional AI governance and provide recommendations for higher education institutions, educators, researchers, and policymakers working to support the ethical and responsible integration of artificial intelligence.