The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
ChatGPT: The End of Online Exam Integrity?
Abstract
This study addresses the significant challenge posed by the use of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations, focusing on how these models can undermine academic honesty by demonstrating their latent and advanced reasoning capabilities. An iterative self-reflective strategy was developed for invoking critical thinking and higher-order reasoning in LLMs when responding to complex multimodal exam questions involving both visual and textual data. The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions. The results indicate that the proposed self-reflective strategy can invoke latent multi-hop reasoning capabilities within LLMs, effectively steering them towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. These findings challenge prior assertions about the limitations of LLMs in multimodal reasoning and emphasise the need for robust online exam security measures such as advanced proctoring systems and more sophisticated multimodal exam questions to mitigate potential academic misconduct enabled by AI technologies.
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In the citing paper
“Sectorfocused scholarship has similarly emphasised that policy alone is insufficient, and that assessment redesign and integrity education are needed to support defensible academic standards in the context of ubiquitous AI tools (Cotton et al., 2024;Susnjak, 2024).”
From Drivers of academic dishonesty and multi-level prevention strategies: an umbrella review (2026) · cites this paper as Susnjak, 2024
In the citing paper
“Concerns have intensified as universities scale online and blended provision and as digital tools broaden the opportunity structure for misconduct, creating new tensions between accessibility, valid assessment and defensible integrity processes (Khan et al., 2022;Susnjak, 2024).”
From Drivers of academic dishonesty and multi-level prevention strategies: an umbrella review (2026) · cites this paper as Susnjak, 2024
In the citing paper
“At student level, reviews repeatedly recommend structured integrity education and processes, but the review evidence positions them as proportionate safeguards rather than as substitutes for learning-centered prevention (Cotton et al., 2024;Susnjak, 2024).”
From Drivers of academic dishonesty and multi-level prevention strategies: an umbrella review (2026) · cites this paper as Susnjak, 2024
Citations
Cites
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 1×
“Others [39–41] have recommended integrating multimodal approaches into exams which venture beyond just text-based questions and additionally combine visual components.”
Yusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554
A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100221 · Cited by 402 · cited 1×
“These concerns around negative trends in academic integrity in online exams were already well advanced when ChatGPT was released, and a new and significant compounding factor was introduced [18–21].”
Can large language models reason about medical questions?
Liévin, V., Hother, C. E., Motzfeldt, A. G., & Winther, O. (2024) · Patterns · doi:10.1016/j.patter.2024.100943 · Cited by 343
Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023) · European Journal of Engineering Education · doi:10.1080/03043797.2023.2213169 · Cited by 336
A systematic review of research on cheating in online exams from 2010 to 2021
Noorbehbahani, F., Mohammadi, A., & Aminazadeh, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10927-7 · Cited by 236
Good proctor or “big brother”? Ethics of online exam supervision technologies
Coghlan, S., Miller, T., & Paterson, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00476-1 · Cited by 219
Calibrated Language Models Must Hallucinate
Kalai, A. T., & Vempala, S. S. (2024) · Proceedings of the 56th Annual ACM Symposium on Theory of Computing · doi:10.1145/3618260.3649777 · Cited by 91 · cited 2×
“LLM hallucination is more of an impediment in exams where questions require a faithful recall of facts from subject areas that are underrepresented in the LLM training datasets [46] which is not an issue in most cases.”
Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions
Han, T., Adams, L. C., Bressem, K. K., Busch, F., Nebelung, S., & Truhn, D. (2024) · JAMA · doi:10.1001/jama.2023.27861 · Cited by 83
Van Wyk, M. M. (2024) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2024.7.1.15 · Cited by 35
Research works printed but not linked: 48
- Crawford, 2020, A systematic review of online examinations: A pedagogical innovation for scalable authentication and integrity, Comput. Educ., 159, 104024, 10.1016/j.compedu.2020.104024 · DOI
- Henderson, 2022, Factors associated with online examination cheating, Assess. Eval. High. Educ., 48, 980, 10.1080/02602938.2022.2144802 · DOI · Paper page
- Dumulescu, 2021, Academic leadership in the time of COVID-19—Experiences and perspectives, Front. Psychol., 12, 648344, 10.3389/fpsyg.2021.648344 · DOI
- Whisenhunt, 2022, Maximizing learning while minimizing cheating: New evidence and advice for online multiple-choice exams, Scholarsh. Teach. Learn. Psychol., 8, 140, 10.1037/stl0000242 · DOI
- Garg, 2022, A systematic literature review on online assessment security: Current challenges and integrity strategies, Comput. Secur., 113, 102544, 10.1016/j.cose.2021.102544 · DOI
- Arnold, 2016, Cheating at online formative tests: Does it pay off?, Internet High. Educ., 29, 98, 10.1016/j.iheduc.2016.02.001 · DOI
- Ahsan, 2021, Contract cheating in higher education: A systematic literature review and future research agenda, Assess. Eval. High. Educ., 47, 523, 10.1080/02602938.2021.1931660 · DOI
- Crook, 2021, How internet essay mill websites portray the student experience of higher education, Internet High. Educ., 48, 100775, 10.1016/j.iheduc.2020.100775 · DOI
- Allen, 2023, A systemic model of academic (mis) conduct to curb cheating in higher education, High. Educ., 87, 1529, 10.1007/s10734-023-01077-x · DOI
- Henderson, 2023, The temptation to cheat in online exams: Moving beyond the binary discourse of cheating and not cheating, Int. J. Educ. Integr., 19, 21, 10.1007/s40979-023-00143-2 · DOI
- Malik, 2023, Impact of academic cheating and perceived online learning effectiveness on academic performance during the COVID-19 pandemic among Pakistani students, Front. Psychol., 14, 1124095, 10.3389/fpsyg.2023.1124095 · DOI
- Newton, 2023, How common is cheating in online exams and did it increase during the COVID-19 pandemic? A systematic review, J. Acad. Ethics, 22, 323, 10.1007/s10805-023-09485-5 · DOI
- Handal, 2001, Academic dishonesty at the graduate level, Ethics Behav., 11, 287, 10.1207/S15327019EB1103_7 · DOI
- Newton, P., and Xiromeriti, M. (2023). ChatGPT performance on multiple choice question examinations in higher education. A pragmatic scoping review. Assess. Eval. High. Educ., 1–18. · DOI
- Ji, Z., Yu, T., Xu, Y., Lee, N., Ishii, E., and Fung, P. (2023). Towards Mitigating Hallucination in Large Language Models via Self-Reflection. arXiv. · DOI
- Farazouli, 2024, Hello GPT! Goodbye home examination? An exploratory study of AI chatbots impact on university teachers’ assessment practices, Assess. Eval. High. Educ., 49, 363, 10.1080/02602938.2023.2241676 · DOI · Paper page
- Richards, 2024, Bob or Bot: Exploring ChatGPT’s Answers to University Computer Science Assessment, ACM Trans. Comput. Educ., 24, 1, 10.1145/3633287 · DOI · Paper page
- Chan, 2023, Unproctored online exams provide meaningful assessment of student learning, Proc. Natl. Acad. Sci. USA, 120, e2302020120, 10.1073/pnas.2302020120 · DOI
- Martino, A., Iannelli, M., and Truong, C. (June, January 28). Knowledge injection to counter large language model (LLM) hallucination. Proceedings of the European Semantic Web Conference 2023, Hersonissos, Greece. · DOI
- Yao, J.Y., Ning, K.P., Liu, Z.H., Ning, M.N., and Yuan, L. (2023). LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples. arXiv.
- Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., and Chen, Y. (2023). Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models. arXiv.
- Chang, Y.C., Wang, X., Wang, J., Wu, Y., Zhu, K., Chen, H., Yang, L., Yi, X., Wang, C., and Wang, Y. (2023). A Survey on Evaluation of Large Language Models. arXiv.
- McKenna, N., Li, T., Cheng, L., Hosseini, M.J., Johnson, M., and Steedman, M. (2023). Sources of Hallucination by Large Language Models on Inference Tasks. arXiv. · DOI · Paper page
- Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q., and Zhang, Y. (2023). Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4. arXiv.
- Schultz, 2022, Perils and promise of online exams, Nat. Rev. Chem., 6, 299, 10.1038/s41570-022-00385-7 · DOI
- Alessio, 2017, Examining the effect of proctoring on online test scores, Online Learn., 21, 146, 10.24059/olj.v21i1.885 · DOI
- Han, 2023, Digital proctoring in higher education: A systematic literature review, Int. J. Educ. Manag., 38, 265
- AlSaad, 2023, Large language models in medical education: Opportunities, challenges, and future directions, JMIR Med. Educ., 9, e48291, 10.2196/48291 · DOI · Paper page
- Stribling, 2024, The model student: GPT-4 performance on graduate biomedical science exams, Sci. Rep., 14, 5670, 10.1038/s41598-024-55568-7 · DOI
- Zhang, 2024, M3exam A multilingual, multimodal, multilevel benchmark for examining large language models, Adv. Neural Inf. Process. Syst., 36, 5484
- Sadasivan, V.S., Kumar, A., Balasubramanian, S., Wang, W., and Feizi, S. (2023). Can AI-Generated Text be Reliably Detected?. arXiv.
- Orenstrakh, M.S., Karnalim, O., Suarez, C.A., and Liut, M. (2023). Detecting LLM-Generated Text in Computing Education: A Comparative Study for ChatGPT Cases. arXiv.
- Kumarage, T., Agrawal, G., Sheth, P., Moraffah, R., Chadha, A., Garland, J., and Liu, H. (2024). A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization. arXiv.
- Pal, A., and Sankarasubbu, M. (2024). Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinations. arXiv. · DOI
- Nori, H., King, N., McKinney, S., Carignan, D., and Horvitz, E. (2023). Capabilities of GPT-4 on Medical Challenge Problems. arXiv.
- Stechly, K., Marquez, M., and Kambhampati, S. (2023). GPT-4 Doesn’t Know It’s Wrong: An Analysis of Iterative Prompting for Reasoning Problems. arXiv.
- Du, Y., Li, S., Torralba, A., Tenenbaum, J., and Mordatch, I. (2023). Improving Factuality and Reasoning in Language Models through Multiagent Debate. arXiv.
- Luo, J., Xiao, C., and Ma, F. (2023). Zero-Resource Hallucination Prevention for Large Language Models. arXiv.
- Creswell, A., and Shanahan, M. (2022). Faithful Reasoning Using Large Language Models. arXiv.
- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2023). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.
- Yeadon, W., and Halliday, D.P. (2023). Exploring durham university physics exams with large language models. arXiv.
- Singla, A. (2023, January 7–11). Evaluating ChatGPT and GPT-4 for Visual Programming. Proceedings of the 2023 ACM Conference on International Computing Education Research, Chicago, IL, USA. · DOI
- Zheng, C., Liu, Z., Xie, E., Li, Z., and Li, Y. (2023). Progressive-Hint Prompting Improves Reasoning in Large Language Models. arXiv.
- Han, 2024, Inductive reasoning in humans and large language models, Cogn. Syst. Res., 83, 101155, 10.1016/j.cogsys.2023.101155 · DOI
- Luo, L., Lin, Z., Liu, Y., Shu, L., Zhu, Y., Shang, J., and Meng, L. (2023). Critique ability of large language models. arXiv.
- Feng, T.H., Denny, P., Wuensche, B., Luxton-Reilly, A., and Hooper, S. (February, January 29). More Than Meets the AI: Evaluating the performance of GPT-4 on Computer Graphics assessment questions. Proceedings of the 26th Australasian Computing Education Conference, Sydney, NSW, Australia.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017, January 4–9). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA.
- Melkonian, H., Bending, Z., and Tomossy, G. (2022, January 28–30). Viva Voce Assessment—Legal Education for the Real World. Proceedings of the 2022 Professional Legal Education Conference: LawTech, Newlaw and NetZero: Preparing for an Uncertain Future, Gold Coast, QLD, Australia. Conference Program.
Cited by
Sorted by citations
Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.04023 · Cited by 337
“Responsible Generative AI Previous works have discussed the ethical implications or concerns associated with ChatGPT (and other LLMs) (Jabotinsky and Sarel, 2022; Susnjak, 2022; Blanco-Gonzalez et al., 2022; Aydın and Karaarslan, 2022; Jeblick et al., 2022).”
BackgroundA Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Wu, J., Yang, S., Zhan, R., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Computational Linguistics · doi:10.1162/coli_a_00549 · Cited by 212
Evangelista, E. D. L. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/15775 · Cited by 124
Critical Thinking in the Age of AI: A Systematic Review of AI’s Effects on Higher Education
Melisa, R., Ashadi, A., Triastuti, A., Hidayati, S., Salido, A., Ero, P. E. L., Marlini, C., Zefrin, Z., & Al Fuad, Z. (2025) · Educational Process International Journal · doi:10.22521/edupij.2025.14.31 · Cited by 117
“For example, Susnjak (2024) investigated ChatGPT's role in maintaining academic integrity during online assessments, suggesting that although AI models can manage complex multimodal questions, they also introduce risks to academic honesty.”
Revell, T., Yeadon, W., Cahilly-Bretzin, G., Clarke, I., Manning, G., Jones, J., Mulley, C., Pascual, R. J., Bradley, N., Thomas, D., et al. (2024) · International Journal for Educational Integrity · doi:10.1007/s40979-024-00161-8 · Cited by 51
Metadata
- Authors
- Susnjak, T., & McIntosh, T.
- Affiliations
- School of Mathematical and Computational Sciences, Massey University, Auckland 0632, New Zealand
- School of Computing, Engineering and Mathematical Science, LaTrobe University, Melbourne, VIC 3086, Australia
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2024-06-17
- Volume
- 14
- Issue
- 6
- Pages
- 656
- ISSN
- 2227-7102
- Cited by
- 189
- References
- 14
- Language
- en
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