Student use of large language model artificial intelligence on a history of mathematics module

Authors

DOI:

https://doi.org/10.21100/msor.v23i2.1540

Keywords:

artificial intelligence, generative AI, ChatGPT, student essays, history of mathematics.

Abstract

This case study assesses experience in autumn 2023 of permitting the use of Large Language Model Artificial Intelligence (AI) in preparing essays on a module in the history of mathematics. As a check on usage and to ensure academic standards, students were required to complete two paragraphs to accompany their essays explaining their use of AI. These generated qualitative and quantitative data on student familiarity with AI, and ability to use it in a thoughtful and ethical manner, which is reported here. Findings were that over 50% of students rejected AI use, and only 9% used it extensively. There was a weak negative correlation between AI use and essay grade, for which student confidence may have been a confounding factor. The most frequent reasons for rejecting AI were ethical, personal (satisfaction and confidence), and the time needed to correct it.

References

Abbas, M. (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). https://doi.org/10.1186/s41239-024-00444-7.

Bender, E.M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery. pp.610-23. https://doi.org/10.1145/3442188.3445922.

Bukar, U.A., Sayeed, M.S., Razak, S.F.A., Yogarayan, S., and Sneesl, R. (2024). Decision-Making Framework for the Utilization of Generative Artificial Intelligence in Education: A Case Study of ChatGPT.” IEEE Access 12 pp.95368–89. https://doi.org/10.1109/ACCESS.2024.3425172.

Chesterman, S. (2024). Good Models Borrow, Great Models Steal: Intellectual Property Rights and Generative AI. Policy and Society, 00(00), pp.1–15. https://doi.org/10.1093/polsoc/puae006.

Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D. and Pineau, J. (2020). Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. Journal of Machine Learning Research 21(248) pp.1–43. http://jmlr.org/papers/v21/20-312.html [accessed 23 December 2024].

O’Dea, X., Tsz Kit Ng, D., O’Dea, M., & Shkuratskyy, V. (2024). Factors affecting university students’ generative AI literacy: Evidence and evaluation in the UK and Hong Kong contexts. Policy Futures in Education, 0(0). https://doi.org/10.1177/14782103241287401.

Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07.

Schei, O.M., Møgelvang, A., and Ludvigsen, K. (2024). Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies. Education Sciences 14(8): 922. https://doi.org/10.3390/educsci14080922.

Strauss, A. and Corbin, J.M. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Thousand Oaks, CA, US: Sage.

Xu, D., Fan, S. and Kankanhalli, M. (2023). Combating Misinformation in the Era of Generative AI Models. Proceedings of the 31st ACM International Conference on Multimedia. New York: Association for Computing Machinery. pp.9291–98. https://doi.org/10.1145/3581783.3612704.

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Published

2025-03-01

How to Cite

Falconer, I. (2025). Student use of large language model artificial intelligence on a history of mathematics module. MSOR Connections, 23(2). https://doi.org/10.21100/msor.v23i2.1540