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Generative AI in higher education: Seeing ChatGPT through universities' policies, resources, and guidelines
Open accessAbstract
The advancements in Generative Artificial Intelligence (GenAI) can provide opportunities for enriching educational experiences, but at the same time raise concerns regarding academic integrity. Many educators have expressed anxiety and hesitation when it comes to integrating GenAI in their teaching practices. Thus, recommendations and guidance from institutions are needed to support instructors in this new and emerging GenAI era. In response to this need, this study explores different U.S. universities' academic policies and guidelines regarding the use of GenAI tools (e.g., ChatGPT) for teaching and learning, and from there, gains understanding of how these universities respond and adapt to the development of GenAI in their academic contexts. Data sources include academic policies, statements, guidelines, and relevant resources provided by the top 100 universities in the U.S. Results show that the majority of these universities adopt an open but cautious approach towards GenAI. Primary concerns lie in ethical usage, accuracy, and data privacy. Most universities actively respond and provide diverse types of resources, such as syllabus templates, workshops, shared articles, and one-on-one consultations; focusing on a range of topics, namely general technical introduction, ethical concerns, pedagogical applications, preventive strategies, data privacy, limitations, and detective tools. The findings provide four practical pedagogical implications for educators when considering GenAI in teaching practices: 1) accepting GenAI presence, 2) aligning GenAI use with learning objectives, 3) evolving curriculum to prevent misuse of GenAI, and 4) adopting multifaceted evaluation strategies. For recommendations toward policy making, the article suggests two possible directions for the use of GenAI tools: 1) establishing discipline-specific policies and guidelines, and 2) managing students' sensitive information in a transparent and careful manner.
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“technology departments exhibit heightened caution due to programming vulnerability”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“while humanities struggle when AI replicates traditional essay genres (Gonsalves, 2025; Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Faculty report feeling underprepared to evaluate AI-assisted work and design valid assessments, particularly those unfamiliar with modern paraphrasing capabilities (Karkoulian et al., 2025; Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Institutions are shifting from restrictive bans toward flexible policy frameworks balancing AI affordances with academic rigor (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Most U.S. universities adopted “open but cautious” stances with faculty-led discretion for discipline-specific relevance (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
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“Current AI integration frameworks reveal significant gaps characterized by resource disparities, training deficits, and persistent “policy voids” (Hanh and Duyen, 2025; Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The most critical gap is the shortage of student-facing resources: while 67% of institutional guidance targets faculty, only 17.8% provides direct student frameworks on ethical boundaries and appropriate use (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This divergence is not simply a matter of competing preferences but reflects fundamentally incompatible assumptions about what constitutes legitimate intellectual work—assumptions that existing policy frameworks have yet to adequately reconcile (Karkoulian et al., 2025; Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In such contexts, even highly motivated educators may hesitate to integrate GenAI, fearing reputational or procedural risks (Wang et al., 2024).”
From Educators’ perceptions of generative AI: Investigating attitudes, barriers and learning needs in higher education (2025) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, out of these 57 universities, 27 adopted a 'stance of Prohibition by Default', where the use of GenAI tools is allowed only if the instructor explicitly permits it, otherwise their use is considered a form of academic dishonesty.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [353]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Yet institutional guidance on how detection outputs should be interpreted, weighted, or contested remains largely absent [47, 76].”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [76]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Prior research - shown in Table 4 - has documented significant technical limitations and bias in AI detection systems, as well as their potential to produce false positives with serious consequences for students [43, 76].”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [76]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Wang et al. [76], who found that while 57% of universities in their dataset mentioned common tools, none explicitly recommended their use.”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [76]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Institutional administrators have converged on an “open but cautious” approach, rejecting total bans while emphasizing faculty-led discretion (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
The cited source says what the claim attributes to it.
In the citing paper
“A critical gap exists in resource distribution: 67% of guidance targets faculty education while only 17.8% provides direct student frameworks (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
The cited source says what the claim attributes to it.
In the citing paper
“Wang et al. [353] found that none of the top-100 universities in the US banned the use of GenAI tools and that 57 of these universities defer the decision to the instructor.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [353]
The cited source says what the claim attributes to it.
In the citing paper
“On the other hand, only a single institution adopted a 'stance of Permissibility by Default'.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [353]
The cited source says what the claim attributes to it.
In the citing paper
“Prior empirical audits of university AI governance have focused on policy presence among elite or researchintensive samples [47, 50, 76, 80] (Table 2, Appendix B), examining primarily the pedagogical dimension: how universities communicate expectations about AI use in coursework.”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [76]
The cited source says what the claim attributes to it.
In the citing paper
“Prior work has shown heterogeneity in how universities define and communicate AI-related expectations, with many institutions relying on vague or decentralized guidance [47, 76].”
From The Due Process Deficit: Auditing AI Governance in U.S. Higher Education (2026) · cites this paper as [76]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The evidence base may reflect “time-lag bias” where early success stories receive publication priority while longer-term outcomes remain undocumented (Wang et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Wang et al., 2024
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Additional concerns such as bias in AI outputs (Lee et al., 2024) and lack of transparency in model decisions (Wang et al., 2024) further compound this hesitancy.”
From Educators’ perceptions of generative AI: Investigating attitudes, barriers and learning needs in higher education (2025) · cites this paper as Wang et al., 2024
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Metadata
- Authors
- Wang, H., Dang, A., Wu, Z., & Mac, S.
- Affiliations
- University of Arizona (US)
- University of Georgia (US)
- Corresponding author
- Wu, Zihao
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 7
- Pages
- 100326
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4403935384
- Semantic Scholar
- 68dc625a7a15107381639f56d33397d0f2c562ce
- Cited by
- 272
- References
- 44
- Citations by year
- 2026: 160 · 2025: 100 · 2024: 12
- FWCI
- 101.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI in higher education, ChatGPT, AI guidelines, academic integrity, AI ethics, data privacy, AI in teaching, AI misuse prevention, curriculum adaptation
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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