Review of using Generative Artificial Intelligence in Physics Education: A case study of KMUTT (Ratchaburi)
Article Sidebar
Main Article Content
Abstract
The rapid advancement of Generative Artificial Intelligence (Gen-AI) has opened new possibilities for enhancing teaching and learning across disciplines, including physics education. This article reviews the potential applications of Gen-AI in undergraduate physics instruction, with a focus on its implementation at King Mongkut’s University of Technology Thonburi (KMUTT), Ratchaburi campus. Two primary areas are explored: (1) the use of Gen-AI to support the development of students’ physics reasoning skills through guided problem-solving and self-explanation strategies, and (2) the feasibility of employing Gen-AI as an offline automated assessment tool to provide timely feedback and reduce instructional workload. The review also addresses key challenges related to pedagogy, data privacy, and language compatibility, particularly in the context of Thai-language instruction. Preliminary evaluations of open-source language models are discussed, highlighting both their potential and current limitations. The findings suggest that with thoughtful integration, Gen-AI can play a transformative role in physics education by promoting deeper conceptual understanding and supporting scalable, personalized learning environments.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Catrambone, R., and Holyoak, K. J. (1989) Overcoming Contextual Limitations on Problem-Solving Transfer. Journal of Experimental Psychology: Learning Memory and Cognition 15(6): 1147 - 1156.
Chi, M.T.H., Feltovich, P.J. and Glaser, R. (1981) Categorization and representation of physics problems by experts and novices. Cognitive Science 5(2): 121 – 152.
Chi, M.T.H. and VanLehn, K.A. (1991) The Content of Physics Self-Explanations. The Journal of the Learning Science 1(1): 69 – 105.
Doucette, D., Clark, R. and Singh, C. (2020) Professional development combining cognitive apprenticeship and expectancy-value theories improves lab teaching assistants' instructional views and practices. Physical Review Physics Education Research 16(2): 020102. doi: 10.1103/PhysRevPhysEducRes.16. 020102.
Gjerde, V., Paulsen, V.H., Holst, B. and Kolst, S.D. (2022). Problem solving in basic physics: Effective self-explanations based on four elements with support from retrieval practice. Physical Review Physics Education Research 18(1): 010136. doi: 10.1103/PhysRevPhysEducRes.18.010136.
Kortemeyer, G. (2023). Toward AI grading of student problem solutions in introductory physics: a feasibility study. Physical Review Physics Education Research 19: 020163.
Kortemeyer, G., Nöhl, J. and Onishchuk, D. (2024). Grading assistance for a handwritten thermodynamics exam using artificial intelligence: An exploratory study. Physical Review Physics Education Research 20: 020144
Larkin, J., McDermott, J., Simon, D.P. and Simon, H.A. (1980) Expert and Novice Performance in Solving Physics Problems. Science 208(4450): 1335 - 1342
Lin, S. and Singh, C. (2010) Using Analogy to Solve a Three-Step Physics Problem. AIP Conference Proceedings 1289: 29 -32. doi: 10.1063/1.3515228.
Lyamuremye, A., Niyonzima, F.N., Mukiza, J., Twagilimana, I., Nyirahabimana, P., Nsengimana, T., Habiyaremye, J.D., Habimana, O. and Nsabayezu, E. (2024) Utilization of artificial intelligence and machine learning in chemistry education: a critical review. Discover Education 3: 95. doi: 10.1007/s44217-024-00197-5.
Mohamed, M.Z.B., Hidayat, R., Suhaizi, N.N.B., Sabri, N.B.M., Mahmud, M.K.H.B. and Baharuddin, S.N.B. (2022). Artificial intelligence in Mathematics education: A systematic literature review. International Electronic Journal of Mathematics Education 17(3): em0694.
Pipatanakul, K., Jirabovonvisut, P., Manakul, P., Sripaisarnmongkol, S., Patomwong, R., Chokchainant, P. and Tharnpipitchai, K. (2023) Typhoon: Thai Large Language Models. arXiv: 2312.13951. doi: 10.48550/arXiv.2312.13951.
Phasook, P., Pranee, J., Saeoung, A., Kerdthaisnog, K., Sukhantharat, K., Chuangkrud, P., Damrongrat, C., Kongyoung, S. (2024). Pathumma-llm-text-1.0.0. Source: https://huggingface.co/nectec/Pathumm a-llm-text-1.0.0. Retrieved date 08 April 2025.
Reinhard, A., Felleson, A., Turner, P.C. and Green, M. (2010) Assessing the impact of metacognitive postreflection exercises on problem-solving skillfulness. Physical Review Physics Education Research 18: 010109. doi: 10.1103/PhysRevPhysEducRes.18.010109.
Soankwan, C., Emarat, N., Arayathanitkul, K., and Chitaree, R. (2007) International Newsletter on Physics Education. Physics Education in Thailand. Source: https://www.researchgate.net/publication/2 37462232_Physics_Education_in_Thailand. Retrieved date 10 April 2025.
Usak, M. (2024). Artificial intelligence in Biology education. Journal of Baltic Science Education 23(5): 806 - 808.
Wink, R. and Bonivento, W.M. (2023). Artificial Intelligence: New Challenges and Opportunities in Physics Education. In: Streit-Bianchi, M., Michelini, M., Bonivento, W., Tuveri, M. New Challenges and Opportunities in Physics Education, Challenges in Physics Education. Springer, Cham. doi: 10.1007/978-3-031-37387-9_27
