Exploring the Effectiveness of Institutional Policies and Regulations for Generative AI Usage in Higher Education
Open accessAbstract
As generative artificial intelligence (Gen AI) becomes more integrated into higher education, institutions struggle to communicate clear and effective policies. This multiple‐methods study examines survey and interview data collected with 124 undergraduates and seven faculty members from two U.S. R1 universities regarding their perceptions of institutional AI policies, supportive resources and challenges. Students reported that while classroom‐level policies are recognised, institutional guidelines remain unclear. Similarly, faculty highlighted the implementation challenges they face due to practical constraints and insufficient training. Additional findings show that students aware of AI policies are less likely to use AI for writing and research; disciplinary and gender differences in institutional resources and policy awareness. Faculty interviews revealed themes such as AI use in research and varied colleague attitudes. Although this study is limited by the number of institutions and sample size, it highlights the need for ongoing research to support policy refinement.
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Staff assume the main responsibility of 'translating' institution-level policy and guidance to local guidance that students can easily follow.
“Staff assume the main responsibility of ' translating ' institution-level policy and guidance to local guidance that students can easily follow (Jiang et al., 2025).”
From Assessment-focused governance mode? Mapping Generative AI policies across 76 UK higher education institutions: Policy framework and recommendations (2026) · cites this paper as Jiang et al., 2025
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“However, as AI becomes more deeply embedded in consequential decisions, such as admissions, grading, and resource allocation, scholars increasingly call for more robust regulatory mechanisms that complement voluntary ethical frameworks (Jiang et al., 2025; Liu et al., 2025).”
Tao, Y., Ryan, M., Bialy, F., & Zhang, X. (2026) · Research Square (preprint) · doi:10.21203/rs.3.rs-10029667/v1
- Authors
- Jiang, Y., Xie, L., & Cao, X.
- Affiliations
- Purdue University West Lafayette (US)
- Texas Tech University (US)
- Corresponding author
- Jiang, Yingying
- Venue
- Higher Education Quarterly
- Publisher
- Wiley
- Published
- 2025-08-15
- Volume
- 79
- Issue
- 4
- ISSN
- 0951-5224 (ISSN-L)
- 1468-2273
- OpenAlex
- W4413348179
- Cited by
- 37
- References
- 10
- Citations by year
- 2026: 37
- FWCI
- 38.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Higher Education Quarterly (publishedVersion, cc-by)
- ThinkTech (Texas Tech University) (submittedVersion, cc-by)
- Funders
- Purdue University
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, institutional AI policies, higher education, AI regulation, policy awareness, student perceptions, teacher perceptions, artificial intelligence in research, disciplinary differences, gender differences, AI training, R1 universities
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