Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education
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The study provides a comprehensive examination of the current status and the prevailing trend of ChatGPT policies in universities worldwide, focusing on whether universities have implemented policies regarding ChatGPT usage and, if so, whether they choose to ban or embrace this technology. Additionally, this study aims to identify factors influencing a university’s decision regarding its ChatGPT policy. Analyzing data from the top 500 universities in the 2022 QS World University Rankings, we found significant variation in ChatGPT policy implementation. Less than one-third of these universities had implemented such policies. Among universities with policies, approximately 67.4% allowed ChatGPT, twice the number of universities that banned it. Most universities that allowed the use of ChatGPT provided flexibility for instructors. We observed a dispersed geographic distribution of policy implementation. Our empirical analysis identified factors positively associated with a university’s likelihood of having a ChatGPT policy, including academic reputation, location in an English-speaking country, and public sentiment. The possibility of banning ChatGPT was positively associated with faculty-student ratio, citations, and English-speaking country location, while negatively correlated with peer universities' bans. We also discussed the challenges faced by universities that adopted or banned ChatGPT.
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A comprehensive examination in May 2023 identified a dispersed geographic distribution of AI policy implementation in higher education, with European and North American universities showing higher responsiveness.
“In terms of AI policy for higher education, a comprehensive examination in May 2023 [42] identified a dispersed geographic distribution of policy implementation in that European and North American universities show higher responsiveness to this new technology tool.”
From AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities (2024) · cites this paper as [42]
The positive perception of GAI as a tool to enhance teaching and learning effectiveness indicated a strategic shift towards leveraging technology to meet educational objectives, highlighting its potential to complement and augment traditional teaching methodologies.
“The positive perception of GAI as a tool to enhance teaching and learning effectiveness indicated a strategic shift towards leveraging technology to meet educational objectives, highlighting its potential to complement and augment traditional teaching methodologies (Xiao et al., 2023; Cheng and YIM, 2024).”
From Generative AI in higher education: A global perspective of institutional adoption policies and guidelines (2025) · cites this paper as Xiao et al., 2023
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Dabiri, A., & Behzadan, A. H. (2026) · AI · doi:10.3390/ai7090363
Whose voices count? Multilingualism, AI policy, and equity at Hispanic-Serving institutions
Tian, Z., & Christiansen, M. S. (2026) · Language Policy · doi:10.1007/s10993-026-09803-8
A double-edged sword: artificial intelligence in scientific writing and authorship
Carvalho, R. F., & Habermann, G. (2026) · Theoretical and Experimental Plant Physiology · doi:10.1007/s40626-026-00415-5
Developing a practical student guide for responsible and reflective use of GenAI in higher education
Khan, Z. R., Krásničan, V., & Hemnani, N. (2026) · International Journal for Educational Integrity · doi:10.1007/s40979-026-00237-7
- Authors
- Xiao, P., Chen, Y., & Bao, W.
- Affiliations
- The University of Melbourne (AU)
- University of Alabama (US)
- University of Connecticut (US)
- Corresponding author
- Xiao, Ping
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4378220797
- Cited by
- 93
- References
- 9
- Citations by year
- 2026: 25 · 2025: 33 · 2024: 30 · 2023: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, generative AI in higher education, AI policy in higher education, artificial intelligence adoption, policy implementation, academic reputation, English-speaking countries, faculty-student ratio, peer effects, public sentiment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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