Psychological and pedagogical problems of using chatGPT in solving physical problems
DOI:
https://doi.org/10.15587/2519-4984.2023.292760Keywords:
artificial intelligence, ChatGPT for solving problems, psychological problems, pedagogical problems, physical task, challenges of ChatGPT in the study of physicsAbstract
All innovative ways of learning are aimed at enabling the average student to learn to think like an expert, to use his/her knowledge like an expert. Traditional physical education, like all-natural sciences, involves the transfer of information to students in lectures, and its consolidation in practical and laboratory classes and in the form of independent homework. At the same time, several aspects of learning are distinguished: conceptual understanding, direct transfer of information, knowledge and basic physical laws. A general drawback of traditional concepts is the low digestibility of the material, which is related to the psychological characteristics of a person: 10% are able to formulate the main ideas of the material that was taught 15 minutes after the explanation, if it is new material for them. All modern educational technologies using interactive methods and various pedagogical methods are aimed at changing the student's psychology and are called upon in various ways and trajectories to reach the sixth level in Bloom's taxonomy - the level of creativity, expert, specialist.
The ability to solve problems in physics is an important element in the system of physical education, because it allows you to achieve a number of goals: students see the practical application of the acquired theoretical knowledge, which makes the learning process more conscious and changes the attitude to learning; contributes to the development of logical thinking, concretization of knowledge, which connects the theoretical lecture material with its practical application. In the process of solving physics problems, a number of personal abilities develop: mental, creative, logical, intelligence, observation, independence and accuracy.
The integration of artificial intelligence (AI) generative models GPT in solving physical problems has attracted considerable attention this year. This article examines the complex interaction between AI and student decision-making, shedding light on the cognitive and emotional factors that must be considered when using AI to solve physical tasks. In addition, the pedagogical implications of incorporating AI into physics education are explored, emphasizing the importance of maintaining a balanced approach that promotes the development of critical thinking, creativity, and ethical reasoning. By diligently addressing these challenges, we can harness the potential of AI to expand problem-solving capabilities while preserving the undeniable value of human intelligence and expertise in scientific research
References
- Mahruf, M., Shohel, C. (2022). E-Learning and Digital Education in the Twenty-First Century. Books on Demand, 308. doi: https://doi.org/10.5772/intechopen.87797
- Perri, L. (2023). What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle. Gartner. Available at: https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2023-gartner-hype-cycle
- Tan, S. K. (2023). ChatGPT and its use cases for Physics Education. Available at: https://www.physicslens.com/chatgpt-and-its-use-cases-for-physics-education
- Kieser, F., Wulff, P., Kuhn, J., K¨uchemann, S. (2023). Educational data augmentation in physics education research using ChatGPT. arXiv preprint arXiv: 2307.14475. doi: doi: https://doi.org/10.48550/arXiv.2307.14475
- Gregorcic, B., Pendrill, A.-M. (2023). ChatGPT and the frustrated Socrates. Physics Education, 58 (3), 035021. doi: https://doi.org/10.1088/1361-6552/acc299
- Küchemann, S., Steinert, S., Revenga, N., Schweinberger, M., Dinc, Y., Avila, K. E., Kuhn, J. (2023). Can ChatGPT support prospective teachers in physics task development? Physical Review Physics Education Research, 19 (2). doi: https://doi.org/10.1103/physrevphyseducres.19.020128
- Krupp, L., Steinert, S., Kiefer-Emmanouilidis, M., Avila, K. E., Lukowicz, P., Kuhn, J., K¨uchemann, S., Karolus, J. (2023). Unreflected Acceptance – Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education. arXiv preprint arXiv:2309.03087. doi: https://doi.org/10.48550/arXiv.2309.03087
- Kortemeyer, G. (2023). Could an artificial-intelligence agent pass an introductory physics course? Physical Review Physics Education Research, 19 (1). doi: https://doi.org/10.1103/physrevphyseducres.19.010132
- Liang, Y., Zou, D., Xie, H., Wang, F. L. (2023). Exploring the potential of using ChatGPT in physics education. Smart Learning Environments, 10 (1). doi: https://doi.org/10.1186/s40561-023-00273-7
- Shamshyn, O. P. (2022). Psychological and pedagogical problems of computerization of solving physics problems in technical high school. Perspektyvy ta innovatsii nauky. Seriia «Pedahohika», 13 (18), 516–528. doi: https://doi.org/10.52058/2786-4952-2022-13(18)-516-528
- Yakubovskyi, P. (2008). Kompetentnisna oriientatsiia u navchanni fizyky. Dyrektor shkoly. Ukraina, 5, 55–59.
- Honcharenko, S. U. (1997). Ukrainskyi pedahohichnyi slovnyk. Kyiv: “Lybid”, 374.
- Conroy, S. (2023). Can ChatGPT solve physics problems? WePC. Available at: https://www.wepc.com/tips/chatpgt-physics-ai-solver/
- Sarathy, V. (2018). Real World Problem-Solving. Frontiers in Human Neuroscience, 12. doi: https://doi.org/10.3389/fnhum.2018.00261
- Hryhorchuk, O. M. (2021). Systema zadach yak zasib profesiino oriientovnoho navchannia fizyky v budivelnykh koledzhakh. Kyiv, 260.
- Brewe, E., Bartley, J. E., Riedel, M. C., Sawtelle, V., Salo, T., Boeving, E. R. et al. (2018). Toward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses. Frontiers in ICT, 5. doi: https://doi.org/10.3389/fict.2018.00010
- Park, J., Lee, L. (2004). Analysing cognitive or non‐cognitive factors involved in the process of physics problem‐solving in an everyday context. International Journal of Science Education, 26 (13), 1577–1595. doi: https://doi.org/10.1080/0950069042000230767
- Rivers, C., Holland, A. (2023). How can generative AI intersect with Bloom’s taxonomy? Available at: https://www.timeshighereducation.com/campus/how-can-generative-ai-intersect-blooms-taxonomy
- Stephen Hawking warns artificial intelligence could end mankind (2014). BBC. Available at: https://www.bbc.com/news/technology-30290540
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Alexandr Shamshin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.