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

TL;DR

This paper analyzes global university policies on generative AI, proposing a framework to guide responsible governance that considers diverse national approaches and institutional priorities.

Contribution

It introduces the UPDF-GAI framework, integrating five domains and 20 themes, to assist universities in developing effective, context-aware GAI policies.

Findings

US universities prioritize faculty autonomy and adaptability.

Japanese universities focus on ethics and risk management.

Chinese universities emphasize technology application and integration.

Abstract

As generative AI (GAI) becomes more integrated into higher education, universities are actively exploring its governance and issuing guidelines to promote responsible use, reflecting varied stages of adoption and orientations. This study undertakes a comparative analysis of current GAI guidelines issued by leading universities in the United States, Japan, and China. Based on these findings, the study proposes a University Policy Development Framework for GAI (UPDF-GAI) to provide both theoretical insights and practical guidance for universities in developing and refining their GAI policies. This study adopts five domains from the extended Technology Acceptance Model. A qualitative content analysis of 124 policy documents from 110 universities was conducted, employing thematic coding to synthesize 20 key themes. These domains and themes form the foundation of the UPDF-GAI framework. The analysis reveals varying priorities and focus of GAI policy of universities in different countries. U.S. universities emphasize faculty autonomy, practical application, and policy adaptability, shaped by cutting-edge research and peer collaboration. Japanese universities take a government-regulated approach, prioritizing ethics and risk management, but provide limited support for AI implementation and flexibility. Chinese universities follow a centralized, government-led model, focusing on technology application over early policy development, while actively exploring GAI integration in education and research. The framework facilitates universities in formulating GAI policies by balancing its values and risks, providing multi-level support, proactively responding to societal impacts, and strengthening self-efficacy. In doing so, it enables the development of sustainable and context-sensitive policies that enhance digital competitiveness and advance preparedness for AI-driven education.