Exploring the potential of using ChatGPT in physics education
Open accessAbstract
The pretrained large language models have been widely tested for their performance on some challenging tasks including arithmetic, commonsense, and symbolic reasoning. Recently how to combine LLMs with prompting techniques has attracted lots of researchers to propose their models to automatically solve math word problems. However, most research works focus on solving math problems at the elementary school level and few works aim to solve problems in science disciplines, e.g., Physics. In this exploratory study, we discussed the potential pedagogical benefits of using ChatGPT in physics and demonstrated how to prompt ChatGPT in solving physics problems. The results suggest that ChatGPT is able to solve some physics calculation problems, explain solutions, and generate new exercises at a human level.
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The findings support cognitive load theory, as discussed by Liang et al. (2023), suggesting that ChatGPT effectively reduces cognitive overload for students by presenting complex information in structured, digestible steps.
“The findings support the cognitive load theory, as discussed by Liang et al. (2023), suggesting that ChatGPT effectively reduces cognitive overload for students by presenting complex information in structured, digestible steps.”
From Investigating the Use of chatGPT as a Tool for Enhancing Critical Thinking and Argumentation Skills in International Relations Debates among Undergraduate Students (2024) · cites this paper as Liang et al. (2023)
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Steinert, S., Avila, K. E., Ruzika, S., Kuhn, J., & Küchemann, S. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00354-1 · Cited by 73 · see citing works
Virvou, M., Tsihrintzis, G. A., & Tsichrintzi, E.-A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120759 · Cited by 55 · see citing works
de la Puente, M., Torres, J., Troncoso, A. L. B., Meza, Y. Y. H., & Carrascal, J. X. M. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00347-0 · Cited by 46 · see citing works
“The findings support the cognitive load theory, as discussed by Liang et al. (2023), suggesting that ChatGPT effectively reduces cognitive overload for students by presenting complex information in structured, digestible steps.”
- Authors
- Liang, Y., Zou, D., Xie, H., & Wang, F. L.
- Affiliations
- Hong Kong Metropolitan University (HK)
- Lingnan University (HK)
- Corresponding author
- Wang, Fu Lee
- Venue
- Smart Learning Environments
- Published
- 2023-10-20
- Volume
- 10
- Issue
- 1
- ISSN
- 2196-7091 (ISSN-L)
- OpenAlex
- W4387812493
- Cited by
- 98
- References
- 4
- Citations by year
- 2026: 27 · 2025: 36 · 2024: 33 · 2023: 2
- FWCI
- 23.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Smart Learning Environments (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Direct Grant (DR23B2)
- Faculty Research Grants (DB23A3 and DB23B2)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, large language models, physics education, prompting techniques, physics problem solving, automatic exercise generation, pedagogical benefits
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