Managing Academic Integrity in the AI Era: ChatGPT Usage and Plagiarism among Malaysian Undergraduates

This study plans investigate the factors influencing plagiarism behaviour among undergraduate from Faculty of Busines and Communication at Universiti Malaysia Perlis (UniMAP). With the advance of technology students are prompt to use AI tools like ChatGPT in completing their assignment. By referring of the Extended Theory of Planned Behaviour (TPB), this study has examined the roles of attitudes, subjective norms, perceived behavioural control, moral obligations, and past violations in contributing to plagiarism behaviour using ChatGPT. A survey was distributed with 331 responses. Data was analyzed using Statistical Pack for Social Science (SPSS). The findings showed that attitudes, perceived behavioural control, moral obligations, and past violations had an influence of plagiaristic behaviour using AI tools such as ChatGPT However, no significant effect was found for subjective norms. This confirmed that policies and guidelines to address the challenge of AI-assisted plagiarism was required to uphold academic integrity in a university setting.

Academic Integrity, Artificial Intelligence in Education, ChatGPT, Plagiarism

1. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179–211. [Google Scholar] [Crossref]

2. Ajzen, I. (2002). Perceived behavioural control, self-efficacy, locus of control, and the theory of planned behaviour. Journal of Applied Social Psychology, Vol. 32, 665-683. [Google Scholar] [Crossref]

3. Alleyne, P., & Phillips, K. (2011). Exploring academic dishonesty among university students in Barbados: An extension to the theory of planned behaviour. Journal of Academic Ethics, 9, 323-338 [Google Scholar] [Crossref]

4. Alser. M & Waisberg. E. (2023). Concerns with the usage of ChatGPT in academia and medicine: a viewpoint. American Journal of Medicine Open, 9 (2023) [Google Scholar] [Crossref]

5. Armitage, C. J., & Conner, M. (1999). The theory of planned behaviour: Assessment of predictive validity and perceived control. British Journal of Social Psychology, 38(1), 35–54. [Google Scholar] [Crossref]

6. Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behaviour. Journal of Research in Personality, 25(3), 285–301. [Google Scholar] [Crossref]

7. Bagozzi, R. P., & Kimmel, S. K. (1995). A comparison of leading theories for the prediction of goaldirected behaviours. British Journal of Social Psychology, 34(4), 437–461. [Google Scholar] [Crossref]

8. Balbay. S, Kilis. S, (2019). Perceived Effectiveness of Turnitin® in Detecting Plagiarism in Presentation Slides. Contemporary Educational Technology 10(10: 25-36. [Google Scholar] [Crossref]

9. Bolin, A. U. (2004). Self-Control, Perceived Opportunity, and Attitudes as Predictors of Academic Dishonesty. The Journal of Psychology, 138(2), 101–114. [Google Scholar] [Crossref]

10. Camara, S. K., Eng-Ziskin, S., Wimberley, L., Dabbour, K. S., & Lee, C. M. (2017). Predicting students’ intention to plagiarize: An ethical theoretical framework. Journal of Academic Ethics, 15(1), 43–58. [Google Scholar] [Crossref]

11. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452. [Google Scholar] [Crossref]

12. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 1–12. [Google Scholar] [Crossref]

13. Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3). [Google Scholar] [Crossref]

14. Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2018). Further understanding factors that explain freshman business students' academic integrity intention and behaviour: Plagiarism and sharing homework. Journal of Business Ethics, 147(1), 197–220. [Google Scholar] [Crossref]

15. Duarte. F. (2025). Number of ChatGPT users. Exploding Topics. Retrieved from: https://explodingtopics.com/blog/chatgpt-users [Google Scholar] [Crossref]

16. Elkhatat, A. M. (2022). Practical randomly selected question exam design to address replicated and sequential questions in online examinations. International Journal for Educational Integrity, 18(1). [Google Scholar] [Crossref]

17. Elkhatat, A. M., Elsaid, K., & Almeer, S. (2021). Some students’ plagiarism tricks, and tips for effective check. International Journal for Educational Integrity, 17(1). [Google Scholar] [Crossref]

18. Engler, J. N., Landau, J. D., & Epstein, M. (2008). Keeping up with the Joneses: Students' perceptions of academically dishonest behaviour. Teaching of Psychology, 35(2), 99–102 [Google Scholar] [Crossref]

19. Fattahi Ardakani, M., Salehi-Abargouei, A., Sotoudeh, A., Esmaeildokht, S., & Bahrevar, V. (2020). Do subjective norms predict the screening of cancer patients’ first-degree relatives? A systematic review and meta-analysis. Asian Pacific Journal of Cancer Prevention, 21(6), 1521–1530. [Google Scholar] [Crossref]

20. Floridi, L. (2023). AI as agency without intelligence: On ChatGPT, large language models, and other generative models. Philosophy & Technology, 36(1). [Google Scholar] [Crossref]

21. Foltýnek T, Meuschke N, Gipp B (2020) Academic Plagiarism Detection. ACM Comput Surv 52(6):1–42. [Google Scholar] [Crossref]

22. Gundu, T. (2023). Chatbots: A framework for improving information security behaviours using ChatGPT. In S. Furnell & N. Clarke (Eds.), Human Aspects of Information Security and Assurance (pp. 418-431). Springer Nature. [Google Scholar] [Crossref]

23. Hosny, M., & Fatima, S. (2014). Attitude of students towards cheating and plagiarism: University case study. Journal of Applied Sciences, 14(8), 748–757 [Google Scholar] [Crossref]

24. Intelligent. (2023). Nearly 1/3 college students have used ChatGPT on written assessments. Intelligent. Retrieved from: https://www.intelligent.com/nearly-1-in-3-college-students-have-used-chatgpt-onwritten-assignments [Google Scholar] [Crossref]

25. Jimenez, K. (2023). Professors are using ChatGPT detector tools to accuse students of cheating. But what if the software is wrong? USA Today. Retrieved from: https://www.usatoday.com/story/news/education/2023/04/12/how-ai-detection-tool-spawned-falsecheating-case-uc-davis/11600777002/ [Google Scholar] [Crossref]

26. Jo, H. (2023). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption. Information Development. Retrieved from: https://doi.org/10.1177/02666669231202764 [Google Scholar] [Crossref]

27. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610.Retrievedfrom: https://doi.org/10.1177/001316447003000308 [Google Scholar] [Crossref]

28. Lo. C.K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Educ. Sci., 13 (4), p. 410. [Google Scholar] [Crossref]

29. Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News, 40(3), 26–29. [Google Scholar] [Crossref]

30. Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. [Google Scholar] [Crossref]

31. McCabe, D. L., & Trevino, L. K. (1997). Individual and contextual influences on academic dishonesty: A multicampus investigation. Research in Higher Education, 38(3), 379–396. [Google Scholar] [Crossref]

32. Moss, S. A., White, B., & Lee, J. (2018). A systematic review into the psychological causes and correlates of plagiarism. Ethics & Behaviour, 28(4), 261–283. [Google Scholar] [Crossref]

33. Namlu, A. G., & Odabasi, H. F. (2007). Unethical computer using behaviour scale: A study of reliability and validity on Turkish university students. Computers & Education, 48, 205–215. [Google Scholar] [Crossref]

34. Naumova, E. N. (2023). A mistake-find exercise: a teacher’s tool to engage with information innovations, [Google Scholar] [Crossref]

35. ChatGPT, and their analogs. Journal of Public Health Policy, 44,173-178. https://doi.org/10.1057/s41271-023-00400-1 [Google Scholar] [Crossref]

36. Ofosu-Ampong, K., Acheampong, B., Kevor, M. O, & Amankwah-Sarfo, F. (2023). Acceptance of Artificial Intelligence (ChatGPT) in education: Trust, innovativeness and psychological need of students. Information and Knowledge Management, 13(4), 37-47. [Google Scholar] [Crossref]

37. Oravec, J. (2023). Artificial Intelligence Implications for Academic Cheating: Expanding the Dimensions of Responsible Human-AI Collaboration with ChatGPT and Bard. Jl. Of Interactive Learning Research, 34(2), 213–237. [Google Scholar] [Crossref]

38. Palmer, A., Pegrum, M., & Oakley, G. (2019). A wake-up call? Issues with plagiarism in transnational higher education. Ethics & Behaviour, 29(1), 23–50. [Google Scholar] [Crossref]

39. Passow, H. J., Mayhew, M. J., Finelli, C. J., Harding, T. S., & Carpenter, D. D. (2006). Factors influencing engineering students' decisions to cheat by type of assessment. Research in Higher Education, 47(6), 643– 684. [Google Scholar] [Crossref]

40. Perkins, M. (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). [Google Scholar] [Crossref]

41. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1–22. [Google Scholar] [Crossref]

42. Stephens, J. M. (2018). Bridging the divide: The role of motivation and self-regulation in explaining the judgment-action gap related to academic dishonesty. Frontiers in Psychology, 9(246), 1–15. [Google Scholar] [Crossref]

43. Stephens, Jason M., Young, Michael F., and Calabrese, Thjomos. (2007). Does Moral Judgement Go Offline When Students Are Online? A Comparative Analysis of Undergraduates’ Beliefs and Behaviours Related to Conventional and Digital Cheating. Retrievedfrom: https://doi.org/10.1080/10508420701519197\ [Google Scholar] [Crossref]

44. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1–10. [Google Scholar] [Crossref]

45. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. [Google Scholar] [Crossref]

46. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education? Smart Learning Environments, 10(1), 15. [Google Scholar] [Crossref]

47. Van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614(7947), 224–226. [Google Scholar] [Crossref]

48. Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in education. Learning, Media and Technology, 48(1), 1–5. [Google Scholar] [Crossref]

49. Yeo, S. (2007). First‐year university science and engineering students’ understanding of plagiarism. [Google Scholar] [Crossref]

50. Higher Education Research & Development, 26(2), 199–216. [Google Scholar] [Crossref]

• The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
• Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
• A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
• The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
• Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria