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Policy·G Space·Evidence-backed gain·Published 2026-07-17

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-national analysis 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 Acc…

TRV-2026-0251Peer-reviewedPermanent record — cite & verify
A framework for developing university policies on generative AI governance: a cross-national comparative study

"RSA Information Security Award for Outstanding Achievement in Government Policy" by cohærence * is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

The quick read

Researchers conducted a cross-national analysis of generative AI guidelines issued by leading universities in the United States, Japan, and China, coding policies across five Technology Acceptance Model domains to identify 20 themes and to build the University Policy Development Framework for Generative AI (UPDF-GAI).

The comparison matters because it shows divergent national orientations  U.S. emphasis on faculty autonomy and adaptability, Japanese emphasis on ethics and risk management, and Chinese emphasis on application  and the proposed framework aims to help institutions balance innovation and risk, though its utility beyond the sampled leading universities remains to be tested.

Main points
  • Analysis covered generative AI guidelines from leading universities in the United States, Japan, and China using an extended Technology Acceptance Model.
  • Five domains were examined and 20 key themes were identified through thematic coding to inform the UPDF-GAI framework.
  • U.S. universities were found to emphasize faculty autonomy, practical application, and policy adaptability.
  • Japanese universities were found to prioritize ethics and risk management while offering limited implementation guidance.
Gain

Researchers developed the University Policy Development Framework for Generative AI to help universities assess priorities and build sustainable governance capacity.

The rundown

The study used an extended Technology Acceptance Model as an analytical lens to examine five domains  Perceived Usefulness and Perceived Ease of Use, Perceived Risk, Facilitating Conditions, Social Influence, and Self-Efficacy  and identified 20 key themes through thematic coding of institutional guidelines.

The comparison found U.S. institutions reflecting environments shaped by cutting-edge research and peer collaboration, Japanese institutions adopting a more government-aligned approach, and Chinese institutions reflecting a centralized, government-led model focusing on technology application rather than early policy formulation while actively exploring integration in education and research.

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