TRANSFORMING MISCONCEPTIONS INTO KNOWLEDGE: THE USE OF ARTIFICIAL INTELLIGENCE IN TEACHING ELECTROMAGNETIC RADIATION

Konstantinos T. Kotsis

Abstract


Electromagnetic radiation (EMR) is a fundamental concept in physics and engineering education, yet it continues to be widely misconstrued due to enduring misconceptions. Conventional teaching methods frequently neglect the abstract characteristics and interdisciplinary significance of EMR, resulting in disjointed knowledge among students. This paper analyzes the incorporation of artificial intelligence (AI) as a transformative instrument to improve the instruction and comprehension of Electronic Medical Records (EMR). The paper posits that AI can enhance personalized learning, promote conceptual comprehension, and rectify misconceptions through adaptive feedback and diverse instructional resources, based on modern educational theories and empirical research. AI-driven simulations, intelligent tutoring systems, and natural language processing interfaces facilitate learners' visualization and interaction with electromagnetic phenomena, thereby connecting abstract theory to practical application. Furthermore, AI enhances formative assessment by providing real-time diagnostics of student cognition, enabling adaptive instructional strategies. The paper emphasizes particular misconceptions, such as conflating all electromagnetic radiation with ionizing radiation or misinterpreting the principle of wave-particle duality, and illustrates how AI tools can facilitate conceptual transformation. Inclusive education is prioritized, with AI improving accessibility for learners with varied needs via customizable content and interface design. The paper highlights AI's capacity to transform physics education by promoting inquiry-based, student-centered learning environments. The results support the intentional incorporation of AI technologies into science curricula to enhance scientific literacy, critical thinking, and engagement with intricate subjects such as EMR in a progressively technological society.

 

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artificial intelligence, electromagnetic radiation, customized learning, physics education, scientific misconceptions

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Abdulmunem, R. A. (2023). Artificial Intelligence in Education. In Z. Khlaif, M. Sanmugam, & J. Itmazi (Eds.), Comparative Research on Diversity in Virtual Learning: Eastern vs. Western Perspectives (pp. 241-255). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3595-3.ch012

Alam, A. (2023). Harnessing the Power of AI to Create Intelligent Tutoring Systems for Enhanced Classroom Experience and Improved Learning Outcomes. In: Rajakumar, G., Du, KL., Rocha, Á. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 171. Springer, Singapore. https://doi.org/10.1007/978-981-99-1767-9_42

Amri, M. M., & Hisan, U. K. (2023). Incorporating AI Tools into Medical Education: Harnessing the Benefits of ChatGPT and Dall-E. Journal of Novel Engineering Science and Technology, 2(02), 34–39. https://doi.org/10.56741/jnest.v2i02.315

Anderson, E. (2018). A Focus on Scientific Inquiry in CTE Through a Green Space. Doctoral Dissertation, Department of Education of The College at Brockport, State University of New York. Retrieved from http://hdl.handle.net/20.500.12648/6188

Arun Kumar, U., Mahendran, G., Gobhinath, S. (2023). A Review on Artificial Intelligence Based E-Learning System. In: Ranganathan, G., Bestak, R., Fernando, X. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-19-2840-6_50

Bandara, W. M. H. K., & Senanayaka, S. G. M. S. D. (2024, April). Use of Artificial Intelligence in Education: A Systematic Review. In 2024 International Research Conference on Smart Computing and Systems Engineering (SCSE) (Vol. 7, pp. 1-5). IEEE. https://doi.org/10.1109/SCSE61872.2024.10550527

Barideaux Jr, K. J. (2017). On the Placement of Retrieval Practice During a Lecture: How Does Lecture Quizzing Affect Memory, Attention, and Test Anxiety? Electronic Theses and Dissertations. 1600. The University of Memphis

Retrieved from https://digitalcommons.memphis.edu/etd/1600

Bonolis, L. (2011). Bruno Rossi and Cosmic Rays: From Earth Laboratories to Physics in Space. ArXiv,1110.6206. https://doi.org/10.48550/arXiv.1110.6206

Bouchard, J. (2025). Approaches to learning and cognitive processes. McGill University (Canada) ProQuest Dissertations & Theses, 2006. NR25104. Retrieved from: https://core.ac.uk/download/pdf/41886669.pdf

Branchetti, L., Cutler, M., Laherto, A., Levrini, O., Palmgren, E. K., Tasquier, G., & Wilson, C. (2018). The I SEE project: An approach to futurize STEM education. Visions for Sustainability, 9, 10-26. https://doi.org/10.13135/2384-8677/2770

Cirkony, C. (2019). Students learning science: representation construction in a digital environment. Environmental Education Research, 26(1), 150–151. https://doi.org/10.1080/13504622.2019.1667307

Costa, M. F., Dorrío, B. V., & Kireš, M. (2013). Proceedings of the 10th International Conference on Hands-on Science, Educating for Science and through Science, Pavol Jozef Šafárik University, Košice, Slovakia. In 10th International Conference on Hands-on Science" Educating for Science and through Science". The Hands-on Science Network. Retrieved from: core.ac.uk/download/55637507.pdf

Cruz-Benito, J. (2022). AI in Education. Special Issue Published by Applied Sciences, MDPI. https://doi.org/10.3390/books978-3-0365-4342-0

Gavrilas, L., & Kotsis, K. T. (2024). Electromagnetic radiation: A comprehensive review of misconceptions. Eurasian Journal of Science and Environmental Education, 4(2), 19-38. https://doi.org/10.30935/ejsee/15719

Gavrilas, L., & Kotsis, K.T. (2023). Assessing elementary understanding of electromagnetic radiation and its implementation in wireless technologies among pre-service teachers. International Journal of Professional Development, Learners, and Learning, 5(2), ep2309. https://doi.org/10.30935/ijpdll/13191

Grace, E. G., Vidhyavathi, P., & Malathi, P. (2023). A study on" AI in education: opportunities and challenges for personalized learning. Industrial Engineering Journal, 52(05), 750-759. https://doi.org/10.36893/iej.2023.v52i05.750-759

Hirano, T., & Hirokawa, J. (2017). Visualization of electromagnetic waves for education. In 2017 IEEE International Conference on Computational Electromagnetics (ICCEM) (pp. 92-93). IEEE. https://doi.org/10.1109/COMPEM.2017.7912835

Ivanjek, L., Shaffer, P., Planinić, M., & McDermott, L. (2020). Probing student understanding of spectra through the use of a typical experiment used in teaching introductory modern physics. Physical Review Physics Education Research, 16(1), 010102. https://doi.org/10.1103/PHYSREVPHYSEDUCRES.16.010102

Jauchem, J. R. (1995). Alleged Health Effects of Electromagnetic Fields: The Misconceptions Continue. Journal of Microwave Power and Electromagnetic Energy, 30(3), 165–177. https://doi.org/10.1080/08327823.1995.11688273

Kavitha, K. B., Pradeep Kumar, T. K., Nithiya, S., & Suguna, A. (2023). Implementation of Artificial Intelligence in Education. International Research Journal of Computer Science, 10(5), 104–108. https://doi.org/10.26562/irjcs.2023.v1005.01

Kotsis, K. T. (2024a). The Qualifications of a High School Physics Teacher Have. EIKI Journal of Effective Teaching Methods, 2(4). https://doi.org/10.59652/jetm.v2i4.270

Kotsis, K. T. (2024b). The Importance of Teaching Electromagnetic Radiation Interaction in High Schools. Journal of Science Education Research, 8(2), 142-151. https://doi.org/10.21831/jser.v8i2.76537

Kotsis, K. T., & Gavrilas, L. (2025). Review of Scientific Literacy of Pre-Service Teachers on Electromagnetic Radiation. European Journal of Contemporary Education and E-Learning, 3(1), 55-64. https://doi.org/10.59324/ejceel.2025.3(1).05

Lampropoulos, G. (2023). Augmented Reality and Artificial Intelligence in Education: Toward Immersive Intelligent Tutoring Systems. In: Geroimenko, V. (eds) Augmented Reality and Artificial Intelligence. Springer Series on Cultural Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-27166-3_8

L'Annunziata, M. F. (2022). Electromagnetic Radiation: Photons. Radioactivity (Third Edition), 709-746. https://doi.org/10.1016/B978-0-323-90440-7.00005-3

Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824-2838. https://doi.org/10.1111/BJET.12861

Martins, I. G. R. (1992). Pupils' and teachers' understanding of scientific information related to a matter of public concern (Doctoral dissertation, Institute of Education, University of London). Retrieved from: https://discovery.ucl.ac.uk/id/eprint/10018666

Mese, I. (2023). The Impact of Artificial Intelligence on Radiology Education in the Wake of Coronavirus Disease 2019. Korean Journal of Radiology, 24(5), 478–479. https://doi.org/10.3348/kjr.2023.0278

Migdanalevros I. & Kotsis K.T., (2021). Literacy of students of the Department of Primary Education regarding radioactivity, International Journal of Educational Innovation, Vol. 3, Issue 3, 136-145.

Mills, K. A., Unsworth, L., & Scholes, L. (2023). Literacy for digital futures: Mind, body, text (p. 274). Taylor & Francis. https://doi.org/10.4324/9781003137368

Morales López, A. I., & Tuzón Marco, P. (2022). Misconceptions, knowledge, and attitudes towards the phenomenon of radioactivity. Science & Education, 31(2), 405-426. https://doi.org/10.1007/S11191-021-00251-W

Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022, March). Empowering teachers with AI: Co-designing a learning analytics tool for personalized instruction in the science classroom. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 1-12). https://doi.org/10.1145/3506860.3506861

Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428

Radanović, I., Garašić, D., Lukša, Ž., Ristić Dedić, Z., Jokić, B., & Sertić Perić, M. (2016). Understanding of photosynthesis concepts related to students’ age. In: Electronic Proceedings of the ESERA 2015 Conference. Science education research: Engaging learners for a sustainable future. Part 1: Learning science: conceptual understanding. University of Helsinki, Helsinki, pp. 271-277. ISBN 978-951-51-1541-6. Retrieved from: https://core.ac.uk/download/53109058.pdf

Rusillo-Magdaleno, A., Ruiz-Ariza, A., Suárez-Manzano, S., Martínez-Redecillas, T. (2023). Artificial Intelligence, Augmented Reality and Education. In: Geroimenko, V. (eds) Augmented Reality and Artificial Intelligence. Springer Series on Cultural Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-27166-3_6

Shankar, P. R. (2022). Artificial intelligence in health professions education. Archives of Medicine and Health Sciences 10(2), 256-261. https://doi.org/10.4103/amhs.amhs_234_22

Suryanto, T., Wibawa, A., Hariyono, H., & Nafalski, A. (2023). Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems. International Journal of Robotics and Control Systems, 3(4), 955-1006. doi:https://doi.org/10.31763/ijrcs.v3i4.1195

Tan, S. (2023). Harnessing Artificial Intelligence for Innovation in Education. In: Learning Intelligence: Innovative and Digital Transformative Learning Strategies. Springer, Singapore. https://doi.org/10.1007/978-981-19-9201-8_8

Tapalova, O., & Zhiyenbayeva, N. (2022). Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-Learning, 20(5), 639-653. https://doi.org/10.34190/ejel.20.5.2597

UBT - University for Business and Technology. (2022). 11th International Conference on Business, Technology and Innovation 2022, UBT International Conference. Retrieved from: https://core.ac.uk/download/551326668.pdf

Zhu, G., Li, L., Xue, M., & Liu, T. (2021). An effective educational tool for straightforward learning of numerical modeling in engineering electromagnetics. Computer Applications in Engineering Education, 29(6), 1554-1566. https://doi.org/10.1002/cae.22409




DOI: http://dx.doi.org/10.46827/ejoe.v10i3.6081

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