The emergence of artificial intelligence (AI) in higher education has sparked numerous discussions about its implications. ChatGPT, a prominent AI conversational model, has attracted significant attention for its ability to generate essays and formulate responses.
Objective
The current study sought to explore how and why students are using ChatGPT, and to examine their perceptions about ChatGPT and academic integrity.
Method
Students were surveyed about the frequency and motivation for ChatGPT use and their views on ChatGPT and academic misconduct. Exploratory factor analyses were conducted to examine patterns of correlations between each of the measures.
Results
Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating.
Conclusion
The current study presents comprehensive data on college students’ ChatGPT usage patterns, attitudes, and perceptions of cheating behavior. The outcomes of this research provide insight into how college students are currently interacting with AI tools.
Teaching Implications
Our findings offer practical insights for universities developing AI policies in the classroom, contributing to the ongoing discourse on AI’s role in higher education by providing accurate information about ChatGPT’s pervasiveness in academia.
AlshaterM. (2022). Exploring the role of artificial intelligence in enhancing academic performance: A case study of ChatGPT. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312358
Baidoo-AnuD.Owusu AnsahL. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
4.
ByrneB. M. (2005). Factor analytic models: Viewing the structure of an assessment instrument from three perspectives. Journal of Personality Assessment, 85(1), 17–32. https://doi.org/10.1207/s15327752jpa8501_02
5.
CottonD. R. E.CottonP. A.ShipwayJ. R. (2024). 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
6.
CreswellA.WhiteT.DumoulinV.ArulkumaranK.SenguptaB.BharathA. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53–65. https://doi.org/10.1109/MSP.2017.2765202
7.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
8.
FabrigarL. R.WegenerD. T.MacCallumR. C.StrahanE. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
9.
FiratM. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning & Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.22
10.
ForoughiB.SenaliM. G.IranmaneshM.KhanfarA.GhobakhlooM.AnnamalaiN.Naghmeh-AbbaspourB. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 1–20. https://doi.org/10.1080/10447318.2023.2226495
11.
GaoC. A.HowardF. M.MarkovN. S.DyerE. C.RameshS.LuoY.PearsonA. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. Npj Digital Medicine, 6(1), 75. https://doi.org/10.1038/s41746-023-00819-6
12.
GusakJ.CherniukD.ShilovaA.KatrutsaA.BershatskyD.ZhaoX.Eyraud-DuboisL.ShlyazhkoO.DimitrovD.OseledetsI.BeaumontO. (2022). Survey on large scale neural network training (arXiv:2202.10435). arXiv. https://doi.org/10.48550/arXiv.2202.10435
13.
HaqueM. U.DharmadasaI.SwornaZ. T.RajapakseR. N.AhmadH. (2022). “I think this is the most disruptive technology”: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. https://doi.org/10.48550/ARXIV.2212.05856.
14.
HarrisL.HarrisonD.McNallyD.FordC. (2020). Academic integrity in an online culture: Do McCabe’s findings hold true for online, adult learners?Journal of Academic Ethics, 18(4), 419–434. https://doi.org/10.1007/s10805-019-09335-3
15.
HsuH. T.LinC. C. (2022). Factors influencing university students’ intention to engage in mobile assisted language learning through the lens of action control theory. Educational Technology & Society, 25(4), 29–42. https://www.jstor.org/stable/48695979
JishnuD.SrinivasanM.DhanunjayG. S.ShamalaR. (2023). Unveiling student motivations: A study of ChatGPT usage in education. ShodhKosh: Journal of Visual and Performing Arts, 4(2). https://doi.org/10.29121/shodhkosh.v4.i2.2023.503
18.
KasneciE.SesslerK.KüchemannS.BannertM.DementievaD.FischerF.GasserU.GrohG.GünnemannS.HüllermeierE.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, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
19.
KatzE.BlumlerJ. G.GurevitchM. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509–523. https://doi.org/10.1086/268109
20.
KungT. H.CheathamM.MedenillaA.SillosC.De LeonL.ElepañoC.MadriagaM.AggabaoR.Diaz-CandidoG.ManingoJ.TsengV. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198
21.
KusunoseK.KashimaS.SataM. (2023). Evaluation of the accuracy of ChatGPT in answering clinical questions on the Japanese society of hypertension guidelines. Circulation Journal, 87(7), 1030–1033. https://doi.org/10.1253/circj.CJ-23-0308
22.
LimJ. S.ZhangJ. (2022). Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
23.
LoC. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
24.
ManapatP. D.AndersonS. F.EdwardsM. C. (2023). Evaluating avoidable heterogeneity in exploratory factor analysis results. Psychological Methods. https://doi.org/10.1037/met0000589
25.
McCabeD. L. (2005). Cheating among college and university students: A North American perspective. International Journal for Educational Integrity, 1(1). https://doi.org/10.21913/IJEI.v1i1.14
26.
McCabeD. L.TrevinoL. K. (1993). Academic dishonesty: Honor codes and other contextual influences. The Journal of Higher Education, 64(5), 522. https://doi.org/10.2307/2959991
27.
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
28.
NewellS. J. (2023). Employing the interactive oral to mitigate threats to academic integrity from ChatGPT. Scholarship of Teaching and Learning in Psychology. https://doi.org/10.1037/stl0000371
RayP. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003
31.
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/.
32.
RettingerD. A.KramerY. (2009). Situational and personal causes of student cheating. Research in Higher Education, 50(3), 293–313. https://doi.org/10.1007/s11162-008-9116-5
33.
Revelle, W. (2019). psych: Procedures for psychological, psychometric, and personality research. R Package Version, 2(9).
34.
SamaanJ. S.YeoY. H.RajeevN.HawleyL.AbelS.NgW. H.SrinivasanN.ParkJ.BurchM.WatsonR.LiranO.SamakarK. (2023). Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery. Obesity Surgery, 33(6), 1790–1796. https://doi.org/10.1007/s11695-023-06603-5
35.
SilvaA. D. O.JanesD. D. S. (2021). The emergence of ChatGPT and its implications for education and academic research in the 21st century. Review of Artificial Intelligence in Education, 2(00), e06. https://doi.org/10.37497/rev.artif.intell.education.v2i00.6
36.
Stuber-McEwenD.WiseleyP.HoggattS. (2009). Point, click, and cheat: Frequency and type of academic dishonesty in the virtual classroom. Online Journal of Distance Learning Administration, 12(3), 1–10.
TeoT.DoleckT.BazelaisP.LemayD. J. (2019). Exploring the drivers of technology acceptance: A study of Nepali school students. Educational Technology Research and Development, 67(2), 495–517. https://doi.org/10.1007/s11423-019-09654-7
39.
Thurstone, L. L. (1947). Multiple factor analysis. University of Chicago Press.
40.
TiwariC. K.BhatM. A.KhanS. T.SubramaniamR.KhanM. A. I. (2023). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21, 333–355. https://doi.org/10.1108/ITSE-04-2023-0061
41.
TliliA.ShehataB.AdarkwahM. A.BozkurtA.HickeyD. T.HuangR.AgyemangB. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), Article 15. https://doi.org/10.1186/s40561-023-00237-x
42.
VairaL. A.LechienJ. R.AbbateV.AlleviF.AudinoG.BeltraminiG. A.BergonzaniM.BolzoniA.CommitteriU.CrimiS.GabrieleG.LonardiF.MaglittoF.PetrocelliM.PucciR.SaponaroG.TelA.VelloneV.Chiesa-EstombaC. M.De RiuG. (2024). Accuracy of ChatGPT-generated information on head and neck and oromaxillofacial surgery: A multicenter collaborative analysis. Otolaryngology–Head and Neck Surgery, 170(6), 1492–1503. https://doi.org/10.1002/ohn.489
43.
VandeheyM.MichaelA.DiekhoffG.LaBeffE. (2007). College cheating: A twenty-year follow-up and the addition of an honor code. Journal of College Student Development, 48(4), 468–480. https://doi.org/10.1353/csd.2007.0043
44.
Vargas-MurilloA. R.Pari-BedoyaI. N. M. D. L. A.Guevara-SotoF. D. J. (2023). Challenges and opportunities of AI-assisted learning: A systematic literature review on the impact of ChatGPT usage in higher education. International Journal of Learning, Teaching and Educational Research, 22(7), 122–135. https://doi.org/10.26803/ijlter.22.7.7
45.
VenkateshV.ThongJ. Y. L.XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
46.
WangY.LiuC.TuY.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116–129.
47.
WuT.HeS.LiuJ.SunS.LiuK.HanQ.-L.TangY. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136. https://doi.org/10.1109/JAS.2023.123618
48.
YuH.GlanzerP. L.JohnsonB. (2017). Why students cheat: A conceptual framework of personal, contextual, and situational factors. In VelliarisD. (Ed.), Handbook of research on academic misconduct in higher education (pp. 35–59). IGI Global.
