Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies.
Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading.
Method: I surveyed 733 undergraduates.
Results: Students reported mixed levels of experience with AI and tended toward nervousness over excitement. Most reported professors addressing AI but not integrating it. While 41% of students had used AI in ways explicitly banned, many more students (59%) reported ambiguous use cases. Students overestimated peer cheating, and this predicted their own cheating, as did general experience with and excitement about AI. Meanwhile, 11% of students reported false accusations, with first-generation students possibly at a higher rate. Pragmatic views about career and inequality may be affecting behaviors. Men consistently reported more involvement with AI than women.
Conclusion: Future research should focus on the hybrid collaboration of humans and AI and how AI might be leveraged to support and scaffold genuine learning.
Teaching Implications: AI will be relevant to many future careers, and students increasingly want it to be part of their education. Academic integrity will be a continuing challenge, and students need transparency.
AbbasM.JamF. A.KhanT. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, 1–22. https://doi.org/10.1186/s41239-024-00444-7
2.
Abd-ElaalE.GamageS. H. P. W.MillsJ. E. (2022). Assisting academics to identify computer generated writing. European Journal of Engineering Education, 47(5), 725–745. https://doi.org/10.1080/03043797.2022.2046709
3.
AlbadaN. A.WoodsV. E. (2025). Giving credit where credit is due: An artificial intelligence contribution statement for research methods writing assignments. Teaching of Psychology, 52(3), 279–284. Advance online publication. https://doi.org/10.1177/00986283241259750
4.
AlenichevA.KingoriP.GrietensK. P. (2023). Reflections before the storm: The AI reproduction of biased imagery in global health visuals. Lancet Global Health, 11(1), E1496–E1498. https://doi.org/10.1016/S2214-109X(23)00329-7
AnsariA. N.AhmadS.BhuttaS. M. (2023). Mapping the global evidence around the use of ChatGPT in higher education: A systematic scoping review. Education and Information Technologies, 29, 11281–11321. https://doi.org/10.1007/s10639-023-12223-4
AyanwaleM. A.NdlovuM. (2024). Investigating factors of students’ behavioral intentions to adopt chatbot technologies in higher education: Perspective from expanded diffusion theory of innovation. Computers in Human Behavior Reports, 14. https://doi.org/10.1016/j.chbr.2024.100396
BondM.KhosraviH.De LaatM.BergdahlN.NegreaV.OxleyE.PhamP.ChongS. W.SiemensG. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 10, 1–41. https://doi.org/10.1186/s41239-023-00436-z
11.
Cambra-FierroJ. J.BlascoM. F.Lopez-PerezM. E.TrifuA. (2024). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies.
12.
CapraroV.LentschA.AcemogluD.AkgunS.AkhmedovaA.BilanciniE.BonnefonJ.Branas-GarzaP.ButeraL.DouglasK. M.EverettJ. A. C.GigerenzerG.GreenhowC.HashimotoD. A.Holt-LunstadJ.JettenJ.JohnsonS.LongoniC.LunnP.VialeR. (2024, May 6). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. ArXiV Preprint. https://doi.org/10.48550/arXiv.2401.05377
CavazosJ. T.HauckK. A.BaskinH. M.BainC. M. (2025). ChatGPT goes to college: Exploring student perspectives on artificial intelligence in the classroom. Teaching of Psychology, 52(3), 357–368. Advance online publication. https://doi.org/10.1177/00986283241268829
15.
ChakaC. (2024). Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts: A literature and integrative hybrid review. Journal of Applied Learning & Teaching, 7(1), 115–126.
16.
ClarkE.AugustT.SerranoS.HaduongN.GururanganS.SmithN. A. (2021). All that's ‘human’ is not gold: Evaluating human evaluation of generated text. Proceedings of the 59th Annual Meeting of the ACL and the 11th International Joint Conference on NLP, 1. https://doi.org/10.18653/v1/2021.acl-long.565
CottonD. R. E.CottonP. A.ShipwayJ. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
DuganL.IppolitoD.KirubarajanA.ShiS.Callison-BurchC. (2023). Real or fake text: Investigating human ability to detect boundaries between human-written and machine-generated text. Proceedings of the Conference on AI, 37(11), 12763–12771.
21.
Gegg-HarrisonW.QuartermanC. (2024). AI Detection's high false positive rates and the psychological and material impacts on students. In MahmudS. (Ed.), Academic integrity in the age of artificial intelligence (pp. 119–219). IGI Global. https://doi.org/10.4018/979-8-3693-0240-8.ch011
GorichanazT. (2023). Accused: How students respond to allegations of using ChatGPT on assessments. Learning: Research and Practice, 9(2), 183–196. https://doi.org/10.1080/23735082.2023.2254787
25.
HerboldS.Hautli-JaniszA.HeuerU.KiktevaZ.TrautschA. (2023, April 24). AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays. ArXiV Preprint. https://doi.org/10.48550/arXiv.2304.14276
IvesJ.Castillo-MontoyaM. (2020). First-generation college students as academic learners: A systematic review. Review of Educational Research, 90(2), 139–178. https://doi.org/10.3102/0034654319899707
28.
JainL. R.MenonV. (2023). AI Algorithmic bias: Understanding its causes, ethical and social implications. IEEE 35th International Conference on Tools for Artificial Intelligence, 460–467. https://doi.org/10.1109/ICTAI59109.2023.00073
29.
JangJ.MoharanaS.CarringtonP.BagelA. (2024). “It’s the only thing I can trust”: Envisioning large language model use by autistic workers for communication assistance. Proceedings of the CHI Conference on Human Factors in Computing Systems, 77, 1–18. https://doi.org/10.1145/3613904.3642894
30.
JiangY.HaoJ.FaussM.LiC. (2024). Detecting ChatGPT-generated essays in large-scale writing assessment: Is there a bias against non-native English speakers?Computers and Education, 217, https://doi.org/10.1016/j.compedu.2024.105070
31.
KasneciE.SesslerK.KuchemannS.BannertM.DementievaD.FischerF.GasserU.GrohG.GunnemannS.HullermeierE.KruscheS.KutyniokG.MichaeliT.NerdelC.PfefferJ.PoquetO.SailerM.SchmidtA.SeidelT.KasneciG. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, https://doi.org/10.1016/j.lindif.2023.102274
32.
KoumpanE.McOwenL. (2024). Revolutionizing talent: The path in 21st century workforce transformation. Human Factors,Business Management and Society, 135, 74–83. https://doi.org/10.54941/ahfe1004932
LimS. C. J.LeeM. F. (2024). Rethinking education in the era of artificial intelligence (AI): Towards future workforce competitiveness and business success. In KwokA. O. J.TehP. (Eds.), Emerging technologies in business (pp. 151–166). Springer. https://doi.org/10.1007/978-981-97-2211-2_7
35.
LiuY.ZhangZ.ZhangW.YueS.ZhaoX.ChengX.ZhangY.HuH. (2023, September 23). ArguGPT: Evaluating, understanding and identifying argumentative essays generated by GPT models. ArXiV Preprint. https://doi.org/10.48550/arXiv.2304.07666
McCabeD. L.TrevinoL. K.ButterfieldK. D. (2001). Cheating in academic institutions: A decade of research. Ethics & Behavior, 11(3), 219–232. https://doi.org/10.1207/S15327019EB1103_2
NewellS. J. (2023). Employing the interactive oral to mitigate threats to academic integrity from ChatGPT. Scholarship of Teaching and Learning in Psychology. Advance online publication. https://doi.org/10.1037/stl0000371
44.
NguyenA.HongY.DangB.HuangX. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in HIgher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593
PerkinsM. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), 0–24. https://doi.org/10.53761/1.20.02.07
51.
PerkinsM.RoeJ.PostmaD.McGaughranJ.HickersonD. (2023, May 29). Game of tones: Faculty detection of GPT-4 generated content in university assessments. ArXiV Preprint. https://doi.org/10.48550/arXiv.2305.18081
52.
RichmondJ. L.NichollsK. (2025). Using generative AI to promote psychological, feedback, and artificial intelligence literacies in undergraduate psychology. Teaching of Psychology, 52(3), 291–297. Advance online publication. https://doi.org/10.1177/00986283241287203
53.
RudolphJ.TanS.TanS. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education. Journal of Applied Learning & Teaching, 6(1), 342–363.
54.
SadasivanV. S.KumarA.BalasubramanianS.WangW.FeiziS. (2024, February 19). Can AI-generated text be reliably detected? ArXiV preprint. https://doi.org/10.48550/arXiv.2303.11156
ShearH. E.BrittonL. L.SchaeferK. A.ThapaB.BergtoldJ. S. (2023). Artificial intelligence and the future of learning and assessment in agricultural and applied economics. Journal of the Agricultural and Applied Economics Association, 2(4), 613–850. https://doi.org/10.1002/jaa2.98
58.
StoneA. (2023). Student perceptions of academic integrity: A qualitative study of understanding, consequences, and impact. Journal of Academic Ethics, 21, 357–375. https://doi.org/10.1007/s10805-022-09461-5
59.
StoneB. W. (2024a, November 20) Generative AI in higher education [Data set] OSF. https://doi.org/10.17605/OSF.IO/RFP5K
60.
StoneB. W. (2024b). Training ethical and skilled use of generative AI as a tool [Conference presentation]. Rocky Mountain Psychological Association, Denver, CO, United States.
WalterY. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 1–29. https://doi.org/10.1186/s41239-024-00448-3
64.
WanY.PuG.SunJ.GarimellaA.ChangK.PengN. (2023, December 1). “Kelly is a warm person, Joseph is a role model”: Gender biases in LLM-generated reference letters. ArXiv Preprint. https://doi.org/10.48550/arXiv.2310.09219
65.
WarschauerM.TsengW.YimS.WebsterT.JacobS.DuQ.TateT. (2023). The affordances and contradictions of AI-generated text for writers of English as a second or foreign language. Journal of Second Language Writing, 62, 1–7. https://doi.org/10.1016/j.jslw.2023.101071
66.
Weber-WulffD.Anohina-NaumecaA.BjelobabaS.FoltynekT.Guerrero-DibJ.PopoolaO.SigutP.WaddingtonL. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(26), 1–39.
YusufA.PervinN.Roman-GonzalezM. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, 1–29. https://doi.org/10.1186/s41239-024-00453-6
70.
ZengZ.ShaL.LiY.YangK.GasevicD.ChenG. (2024). Toward automatic boundary detection for human-AI collaborative hybrid essay in education. Proceedings of the 38th Conference on AI, 38(20), 22502–22510. https://doi.org/10.1609/aaai.v38i20.30258
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