Mapping the learning styles of pre-service enviromental science education in interaction with artificial intelligence on the topic of electric fields
Abstract
The integration of Artificial Intelligence (AI) in education offers new opportunities to address complex science concepts, yet its interaction with learning styles remains underexplored. Objectives: This study aimed to identify the learning styles of pre-service environmental science teachers and examine how AI-based instruction supports their understanding of electric fields. Using a mixed-methods design, 72 undergraduate students completed the VARK questionnaire, pre- and post-tests on electric field concepts, and participated in interviews. The findings showed significant improvement in conceptual understanding after AI-based learning, with visual and kinesthetic learners benefiting most from simulations and interactive tasks, while aural and read/write learners showed limited gains. Implications: The study highlights the potential of AI to enhance learning through multimodal engagement, but also emphasises the need for inclusive designs that move beyond learning styles toward broader pedagogical frameworks.
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Chen, L., Xie, H., Zou, D., & Hwang, G. J. (2022). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100052. https://doi.org/10.1016/j.caeai.2022.100052
Chen, L., Huang, R. H., & Spector, J. M. (2023). Artificial intelligence in education: Promises and challenges. Educational Technology Research and Development, 71(1), 1–7. https://doi.org/10.1007/s11423-022-10162-9
Coffield, F. (2023). Why the learning styles myth persists. Teaching in Higher Education, 28(5), 623–635. https://doi.org/10.1080/13562517.2022.2103502
Cuevas, J. (2020). Is learning styles-based instruction effective? A comprehensive analysis. Journal of Educational Psychology, 112(2), 253–268. https://doi.org/10.1037/edu0000365
Cuevas, J. (2023). Re-examining the validity of learning styles theory: Implications for educational practice. Frontiers in Psychology, 14, 1173452. https://doi.org/10.3389/fpsyg.2023.1173452
Dai, Y., Chen, Y., & Wang, X. (2023). Intelligent tutoring systems for physics education: A review. Journal of Science Education and Technology, 32(6), 723–740. https://doi.org/10.1007/s10956-023-10024-4
Dekker, S., & Jolles, J. (2023). Do learning styles matter? Investigating the role of modality preferences in digital learning. Learning and Instruction, 84, 101745. https://doi.org/10.1016/j.learninstruc.2022.101745
DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications (5th ed.). Sage.
Fiorella, L., & Mayer, R. E. (2021). Eight ways to promote generative learning. Educational Psychology Review, 33(4), 1107–1133. https://doi.org/10.1007/s10648-020-09586-2
Graesser, A. C., Hu, X., Nye, B. D., & Sottilare, R. A. (2023). Intelligent tutoring systems: Past, present, and future. International Journal of Artificial Intelligence in Education, 33(1), 1–20. https://doi.org/10.1007/s40593-022-00291-6
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171. https://doi.org/10.1016/j.compedu.2016.12.006
Kirschner, P. A. (2021). The learning styles neuromyth: Lack of evidence, persistence, and implications. Educational Psychology, 41(3), 231–243. https://doi.org/10.1080/01443410.2020.1864040
Knoll, A. R., Otani, H., Skeel, R. L., & Van Horn, K. R. (2021). Learning styles: Concepts and evidence. Frontiers in Psychology, 12, 640197. https://doi.org/10.3389/fpsyg.2021.640197
Kontra, C., Lyons, D. J., Fischer, S. M., & Beilock, S. L. (2022). Embodied learning in science education. Trends in Cognitive Sciences, 26(8), 677–689. https://doi.org/10.1016/j.tics.2022.05.006
Kozulin, A. (2022). Vygotsky’s educational theory in cultural context revisited. Cambridge Journal of Education, 52(3), 341–356. https://doi.org/10.1080/0305764X.2021.1973821
Kukul, V. (2024). Revisiting the effectiveness of VARK in higher education. Education and Information Technologies, 29(1), 233–250. https://doi.org/10.1007/s10639-023-12058-0
Lee, M. K., & Zhai, X. (2024). Multimodal AI in education: The next frontier. British Journal of Educational Technology, 55(2), 351–370. https://doi.org/10.1111/bjet.13325
Lee, T. D., Chou, P. N., & Huang, Y. M. (2023). Adaptive AI tutoring systems for electromagnetism. Journal of Educational Computing Research, 61(3), 521–544. https://doi.org/10.1177/07356331221149020
Luckin, R., & Cukurova, M. (2023). Designing AI for education: Classroom implications. Nature Human Behaviour, 7(5), 617–627. https://doi.org/10.1038/s41562-023-01523-2
Mayer, R. E. (2020). Multimedia learning. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 305–321). Routledge.
Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781316941355
Miftah, M., Pratiwi, I. A., & Wulandari, R. (2024). Addressing misconceptions in electromagnetism using digital simulations. Physics Education, 59(2), 025014. https://doi.org/10.1088/1361-6552/acd245
Newton, P. M., & Miah, M. (2017). Evidence-based higher education: Is the learning styles ‘myth’ important? Frontiers in Psychology, 8, 444. https://doi.org/10.3389/fpsyg.2017.00444
Nyaaba, N., Boateng, R., & Mingle, J. (2024). Teacher education and AI: Preparing future educators. Teaching and Teacher Education, 129, 104070. https://doi.org/10.1016/j.tate.2023.104070
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi.org/10.1111/j.1539-6053.2009.01038.x
Roll, I., & Wylie, R. (2024). Evolution of intelligent tutoring systems: Current status and future directions. Computers & Education, 195, 104700. https://doi.org/10.1016/j.compedu.2023.104700
Sweller, J. (2019). Cognitive load theory and educational design: Recent developments. Educational Psychology Review, 31(2), 261–276. https://doi.org/10.1007/s10648-019-09465-5
Sweller, J. (2020). Cognitive load theory. Springer. https://doi.org/10.1007/978-3-030-47860-5
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Tondeur, J., Scherer, R., & Siddiq, F. (2023). Preparing pre-service teachers for technology integration. Journal of Computer Assisted Learning, 39(1), 95–110. https://doi.org/10.1111/jcal.12692
Treagust, D. F. (2021). Diagnostic assessment in science education. Studies in Science Education, 57(2), 145–164. https://doi.org/10.1080/03057267.2021.1888043
Vakili, V., & Dianati, S. (2024). Adaptive learning analytics for personalised education. Computers in Human Behavior, 152, 107180. https://doi.org/10.1016/j.chb.2023.107180
Yeo, S., & Gilbert, J. K. (2023). Addressing misconceptions in electric field concepts. International Journal of Science Education, 45(1), 1–22. https://doi.org/10.1080/09500693.2022.2162007
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2023). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1–27. https://doi.org/10.1186/s41239-023-00423-5
Zhai, X., Chu, H., & Lee, M. (2023). Multimodal AI for inclusive learning environments. Computers & Education: Artificial Intelligence, 4, 100119. https://doi.org/10.1016/j.caeai.2023.100119
Zhang, Y., Xu, H., & Chen, J. (2024). Student misconceptions in electric field learning: A review. Physical Review Physics Education Research, 20(1), 010101. https://doi.org/10.1103/PhysRevPhysEducRes.20.010101
Authors
Amrullah, J. D. R., Ahmad, N., & Shilla, R. A. (2025). Mapping the learning styles of pre-service enviromental science education in interaction with artificial intelligence on the topic of electric fields. Journal of Environment and Sustainability Education, 3(3), 377–386. https://doi.org/10.62672/joease.v3i3.118
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