Designing A Formative Assessment Framework For Electromagnetism Learning Through Phet Simulations And Artificial Intelligence
Keywords:
formative assessment, electromagnetism, PhET simulations, artificial intelligence, physics education, conceptual monitoring, engineering students, CSEM, learning analytics, feedback designAbstract
This article develops a formative-assessment-oriented framework for teaching electromagnetism with the support of PhET simulations and artificial intelligence. Rather than focusing only on end-of-unit achievement, the paper treats assessment as a continuous process for diagnosing misconceptions, tracking reasoning patterns, and shaping instructional response. The need for such a framework arises from the well-documented instability of students’ understanding of electric field, potential, induction, and vector relations. The proposed model integrates CSEM-informed diagnostics, simulation-based evidence gathering, AI-mediated feedback, reflective explanation, and teacher validation. Methodologically, the study outlines a mixed assessment design that combines item-level diagnostics, prompt-response traces, short reflections, and normalized gain analysis.
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. 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
[11] [11] 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.
[12] [12] C. E. Wieman, W. K. Adams, and K. K. Perkins, “PhET: Simulations that enhance learning,” Science, vol. 322, no. 5902, pp. 682–683, 2008.
[13] [13] N. D. Finkelstein et al., “When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment,” Physical Review Special Topics - Physics Education Research, vol. 1, no. 1, p. 010103, 2005.
[14] [14] E. Mazur, Peer Instruction: A User’s Manual. Upper Saddle River, NJ, USA: Prentice Hall, 1997.
[15] [15] J. Sweller, “Cognitive load during problem solving: Effects on learning,” Cognitive Science, vol. 12, no. 2, pp. 257–285, 1988.
