A Framework for Developing University Policies on Generative AI Governance: A Cross-national Comparative Study

As generative AI (GAI) becomes increasingly embedded in higher education, universities worldwide are developing policies to govern its ethical, pedagogical, and institutional use. However, these policies vary across national and institutional contexts. We undertake a cross-nationalanalysis of GAI guidelines issued by leading universities in the United States, Japan, and China, identifying key policy orientations and proposing a structured framework to support policy development. Using an extended Technology Acceptance Model as an analytical lens, we examine five domains Perceived Usefulness and Perceived Ease of Use, Perceived Risk, Facilitating Conditions, Social Influence, and Self-Efficacy, and identify 20 key themes through thematic coding. Together, these findings inform the development of the University Policy Development Framework for Generative AI (UPDF-GAI). U.S. universities emphasize faculty autonomy, practical application, and policy adaptability, reflecting environments shaped by cutting-edge research and peer collaboration. The Japanese universities analyzed adopt a more government-aligned approach, prioritizing ethics and risk management, but offering comparatively limited guidance on AI implementation and flexibility. The Chinese universities in the sample reflect a centralized, government-led model, focusing on technology application rather than early policy formulation, while actively exploring GAI integration in education and research. Based on these insights, the study proposes the UPDF-GAI, integrating technological, organizational, and social dimensions of policy formation. The framework provides a structured approach for universities to assess policy priorities, navigate tensions between innovation and risk, and strengthen institutional capacity for sustainable GAI governance, contributing to the evolving discourse on AI governance in higher education.