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Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education

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arxiv 2305.18617 v1 pith:R3MTASWB submitted 2023-05-25 cs.CY cs.AIcs.LG

classification cs.CYcs.AIcs.LG
keywords chatgptuniversitiespoliciesuniversitycountryempiricalpolicyanalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence, content, and issuance dates. Specifically, we analyzed the top 500 universities according to the 2022 QS World University Rankings. Our findings indicate that there is significant variation in university policies. Less than one-third of the universities included in the study had implemented ChatGPT policies. Of the universities with ChatGPT policies, approximately 67 percent embraced ChatGPT in teaching and learning, more than twice the number of universities that banned it. The majority of the universities that ban the use of ChatGPT in assessments allow individual instructors to deviate from this restrictive policy. Our empirical analysis identifies several factors that are significantly and positively correlated with a university's likelihood of having a ChatGPT policy, including the university's academic reputation score, being in an English-speaking country, and the general public attitudes toward ChatGPT. In addition, we found that a university's likelihood of having a ban policy is positively associated with faculty student ratio, citations, and the English-speaking country dummy, while negatively associated with the number of peer universities within the same country that have banned ChatGPT. We discuss the challenges faced by universities based our empirical findings.

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Cited by 2 Pith papers

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  1. EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers

    cs.AI 2026-08 conditional novelty 6.0 of 10

    EduZone is a new evaluation framework and 5.2K-prompt dataset showing that LLMs are substantially more vulnerable to education-specific risks and adaptive multi-turn attacks than to conventional safety risks.

  2. A Comparative Analysis of Institutional and Course Generative AI Policies within Higher Education: Implications for Instruction in Computing Education

    cs.CY 2026-07 conditional novelty 5.0 of 10

    At the same U.S. R1 universities, institutional GenAI guidance is more pro-use while computing course syllabi stay largely guarded, and an instructor-centered adoption framework is proposed.

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