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.
“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
The Prevention Strategies section (p. 9) reports greater caution in the policies of technology-oriented universities and suggests that programming homework may raise concerns about GenAI-enabled academic dishonesty. It does not establish that technology departments are more cautious or that programming vulnerability is the cause. (LLM, conf=0.87)
Experimental — accuracy not yet measuredHumanities struggle when AI replicates traditional essay genres.
“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
The Literature review (p. 2) discusses AI-generated academic writing and concerns about plagiarism, while the teaching implications (p. 10) recommend adapting assignments to address misuse. These passages support a general challenge for writing-based teaching, but they do not identify humanities specifically or say that AI replicates traditional essay genres. (LLM, conf=0.78)
Experimental — accuracy not yet measuredFaculty report feeling underprepared to evaluate AI-assisted work and design valid assessments, particularly those unfamiliar with modern paraphrasing capabilities.
“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
The Literature review (p. 2) reports that some instructors hesitate to use GenAI because of limited guidance and that faculty called for more resources to improve AI literacy. The Content Analysis (p. 8) discusses how paraphrasing undermines AI detectors, and the Implications (pp. 10–11) recommend assessment strategies. The paper does not report that faculty feel underprepared to evaluate AI-assisted work or design valid assessments, or that those unfamiliar with paraphrasing feel particularly underprepared. (LLM, conf=0.79)
Experimental — accuracy not yet measuredInstitutions are shifting from restrictive bans toward flexible policy frameworks balancing AI affordances with academic rigor.
“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
The policy findings (p. 5) describe flexible approaches among the surveyed U.S. universities: none had a complete ban, and most left decisions to instructors. Section 5.1 (p. 5) also describes restrictions motivated by academic-integrity concerns. But the study reports policies at a point in time, not a documented shift from earlier bans. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMost U.S. universities adopted "open but cautious" stances with faculty-led discretion for discipline-specific relevance.
“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
The Conclusion (p. 11) reports an open yet cautious attitude and says 57 of 104 universities encouraged instructors to set policies suited to their disciplines. The study examined top-ranked U.S. universities, so its findings do not establish the stance of most U.S. universities overall. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Current AI integration frameworks reveal significant gaps characterized by resource disparities, training deficits, and persistent policy voids.
“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
Section 5.3.2 (p. 9) reports differences in resources across university types, and Section 5.2.2 (p. 7) finds that 38.5% offered publicly available workshops, while noting that others may be internal. Section 4.3 (p. 5) reports that 35.6% had not clearly announced a GenAI policy or stance. These findings support uneven resources and policy gaps, but do not establish institution-wide training deficits or that the policy gaps are persistent. (LLM, conf=0.84)
Experimental — accuracy not yet measuredInstitutional guidance is imbalanced: 67% targets faculty while only 17.8% provides direct student frameworks on ethical boundaries and appropriate use.
“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
Section 5.2.1 (p. 6) reports that 67.4% of the universities had resources explicitly designed for faculty and instructors, while 17.8% had resources aimed at students. It says fewer resources guide students on appropriate GenAI use, but does not establish that the 17.8% provide frameworks specifically covering ethical boundaries. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe divergence between students and faculty reflects fundamentally incompatible assumptions about what constitutes legitimate intellectual work, which existing policy frameworks have yet to adequately reconcile.
“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
Section 6.2 (p. 11) says instructors face challenges aligning policies with students’ needs and recommends refining guidelines for specific teaching contexts. That supports the claim that policy frameworks need work, but the paper does not establish that students and faculty hold fundamentally incompatible assumptions about legitimate intellectual work. (LLM, conf=0.78)
Experimental — accuracy not yet measuredEven highly motivated educators may hesitate to integrate GenAI due to fears of reputational or procedural risks.
“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
The Literature review (p. 2) reports that some instructors hesitate to embrace GenAI because of ethical concerns and inadequate institutional guidance. The paper does not establish that these instructors are highly motivated or that they fear reputational risks; its discussion of policy challenges does not specifically identify fear of procedural risks as their reason for hesitating. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOut 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.
“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]
Section 5.1 (p. 5) reports that 27 of the 57 ‘instructor decides’ universities adopted Prohibition by Default, permitting GenAI use only with explicit instructor approval. It says unapproved use in homework and essays is generally not allowed and may constitute plagiarism; the claim overstates this as invariably being considered academic dishonesty. (LLM, conf=0.88)
Experimental — accuracy not yet measuredInstitutional guidance on how AI detection outputs should be interpreted, weighted, or contested remains largely absent.
“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]
Section 5.2.3 (p. 8) reports that none of the universities studied regarded AI detectors as completely reliable or supported using them to determine plagiarism. That supports caution about the weight given to detection outputs, but the paper does not establish that guidance on interpreting or contesting specific outputs is largely absent; it also documents some institutional guidance on detector use. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPrior research 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.
“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]
Section 5.2.3 (p. 8) reports that universities do not regard AI detectors as completely reliable and describes how minor paraphrasing can substantially reduce detection accuracy. Section 6.1 (p. 11) urges caution when accusing students. The cited paper does not document bias in detection systems or specifically link false positives to serious consequences for students. (LLM, conf=0.86)
Experimental — accuracy not yet measuredWang et al. found that while 57% of universities in their dataset mentioned common tools, none explicitly recommended their use.
“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]
Section 5.2.3 (p. 8) reports that 59 of 104 universities (56.7%, approximately 57%) discussed common AI-detection tools. It also says none supported instructors using those tools to evaluate academic integrity or determine plagiarism, but it does not establish that none recommended their use for any purpose. (LLM, conf=0.86)
Experimental — accuracy not yet measuredInstitutional administrators have converged on an open but cautious approach, rejecting total bans while emphasizing faculty-led discretion.
“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
67% of guidance targets faculty education while only 17.8% provides direct student frameworks.
“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
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.
“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]
Only a single institution adopted a 'stance of Permissibility by Default'.
“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]
Prior empirical audits of university AI governance have focused on policy presence among elite or research-intensive samples, examining primarily the pedagogical dimension.
“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]
Prior work has shown heterogeneity in how universities define and communicate AI-related expectations, with many institutions relying on vague or decentralized guidance.
“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 evidence base may reflect "time-lag bias" where early success stories receive publication priority while longer-term outcomes remain undocumented.
“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
The Literature review (p. 2) identifies a gap in research on universities’ GenAI policies, while Data Collection (p. 4) notes that those policies may change after April 2024. Neither addresses publication priority for early success stories or undocumented longer-term outcomes. Searches for “time-lag bias” and those related terms found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredBias in AI outputs and lack of transparency in model decisions compound educators' hesitancy to adopt GenAI.
“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
The Literature review (p. 2) discusses instructors’ hesitation, but attributes it to inadequate institutional guidance and ethical concerns, not opaque model decisions. The paper’s mention of transparency (p. 6) concerns university policy-making. A search for model-decision transparency and related terms found no passage addressing the attributed concern. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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- 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
- 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 in higher education, ChatGPT, AI guidelines, academic integrity, AI ethics, data privacy, AI in teaching, AI misuse prevention, curriculum adaptation
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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.
Statistics and arithmetic are recomputed, not judged.
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