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A university framework for the responsible use of generative AI in research

S.D. Smith et al.

Journal of Higher Education Policy and Management2025https://doi.org/10.1080/1360080x.2025.2509187preprint
AJG 1ABDC B
Weight
0.48

Abstract

Generative Artificial Intelligence (generative AI) poses both opportunities and risks for the integrity of research. Universities must guide researchers in using generative AI responsibly, and in navigating a complex regulatory landscape subject to rapid change. By drawing on the experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of generative AI. We provide guidance to help distil the diverse regulatory environment into a principles-based position statement. Further, we explain how a position statement can then serve as a foundation for initiatives in training, communications, infrastructure, and process change. Despite the growing body of literature about AI's impact on academic integrity for undergraduate students, there has been comparatively little attention on the impacts of generative AI for research integrity, and the vital role of institutions in helping to address those challenges. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework for so doing.

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https://doi.org/https://doi.org/10.1080/1360080x.2025.2509187

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@article{s.d.2025,
  title        = {{A university framework for the responsible use of generative AI in research}},
  author       = {S.D. Smith et al.},
  journal      = {Journal of Higher Education Policy and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/1360080x.2025.2509187},
}

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A university framework for the responsible use of generative AI in research

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.