Developing Technical University Students’ Conceptual Understanding of Electromagnetism Through Artificial Intelligence and Phet Simulations
Keywords:
electromagnetism, physics education, PhET interactive simulations, artificial intelligence, conceptual understanding, engineering education, inquiry-based learning, formative assessment, CSEM, n-gainAbstract
This article examines the didactic potential of integrating PhET simulations with artificial intelligence-based pedagogical support in teaching electromagnetism to students of technical higher education institutions. The relevance of the study stems from the fact that students’ understanding of such concepts as electric field, potential, induction, and charge interaction is often fragmented and insufficiently structured. The paper proposes an instructional model that combines PhET-based visualization with inquiry-based tasks, AI-supported feedback, reflection, and formative assessment. In addition, a methodological design is outlined on the basis of pre-tests, post-tests, CSEM-type diagnostic questions, normalized gain (n-gain), and qualitative analysis. The study argues that the integration of AI and PhET represents a promising approach to deepening conceptual understanding in engineering education.
References
[1] [1] S. Freeman, S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, “Active learning increases student performance in science, engineering, and mathematics,” Proceedings of the National Academy of Sciences, vol. 111, no. 23, pp. 8410–8415, 2014. doi: 10.1073/pnas.1319030111
[2] [2] PhET Interactive Simulations, “Research,” University of Colorado Boulder. [Online]. Available: https://phet.colorado.edu/en/research
[3] [3] H. J. Banda and J. Nzabahimana, “Effect of integrating physics education technology simulations on students' conceptual understanding in physics: A review of literature,” Physical Review Physics Education Research, vol. 17, no. 2, p. 023108, 2021. doi: 10.1103/PhysRevPhysEducRes.17.023108
[4] [4] M. J. P. Dantic and A. R. Fularon, “PhET interactive simulation approach in teaching electricity and magnetism among science teacher education students,” Journal of Science and Education, vol. 3, no. 2, pp. 82–91, 2022. doi: 10.56003/jse.v2i2.101
[5] [5] D. P. Maloney, T. L. O’Kuma, C. J. Hieggelke, and A. Van Heuvelen, “Surveying students' conceptual knowledge of electricity and magnetism,” American Journal of Physics, vol. 69, no. S1, pp. S12–S23, 2001. doi: 10.1119/1.1371296
[6] [6] O. Aquines Gutiérrez, R. K. Galloway, A. Santos, H. Martínez-Huerta, and H. González, “Assisted discovery based learning of the electric force with scaffolding for novice students,” Education Sciences, vol. 12, no. 4, p. 269, 2022. doi: 10.3390/educsci12040269
[7] [7] UNESCO, Guidance for Generative AI in Education and Research. Paris: UNESCO, 2023.
[8] [8] J. Lademann, J. Henze, and S. Becker-Genschow, “Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables,” Physical Review Physics Education Research, vol. 21, no. 1, p. 010147, 2025. doi: 10.1103/PhysRevPhysEducRes.21.010147
[9] [9] S. Küchemann, S. Steinert, J. Kuhn, K. Avila, and S. Ruzika, “Large language models—Valuable tools that require a sensitive integration into teaching and learning physics,” The Physics Teacher, vol. 62, no. 5, pp. 400–402, 2024. doi: 10.1119/5.0212374
[10] [10] A. Younis, “Advancing learning outcomes in physics education through artificial intelligence integration,” European Journal of Education and Pedagogy, vol. 6, no. 5, pp. 73–79, 2025. doi: 10.24018/ejedu.2025.6.5.996
[11] [11] A. Mbonyiryivuze, L. L. Yadav, and M. M. Amadalo, “Students' conceptual understanding of electricity and magnetism and its implications: A review,” African Journal of Educational Studies in Mathematics and Sciences, vol. 15, no. 2, pp. 55–67, 2019. doi: 10.4314/ajesms.v15i2.5
[12] [12] D. M. Ismoilov, “Development of a methodological model for teaching physics to technical university students based on the methods of scientific cognition,” American Journal of Technology Advancement, vol. 2, no. 3, pp. 40–46, 2026.
[13] [13] D. M. Ismoilov, “Development of methods of scientific knowledge at the modern stage,” in Proc. 4th Int. Sci. Pract. Conf. Fundamental and Applied Research in the Modern World, Boston, USA: BoScience Publisher, 2020.
[14] [14] R. R. Hake, “Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses,” American Journal of Physics, vol. 66, no. 1, pp. 64–74, 1998.
[15] [15] C. E. Wieman, W. K. Adams, and K. K. Perkins, “PhET: Simulations that enhance learning,” Science, vol. 322, no. 5902, pp. 682–683, 2008.
