Mapping the learning styles of pre-service enviromental science education in interaction with artificial intelligence on the topic of electric fields

Authors

  • Jadnika Dwi Rakhmawan Amrullah Universitas Jember, Indonesia
  • Nur Ahmad Universitas Jember, Indonesia
  • Rhischa Assabet Shilla The University of Sydney, Australia

DOI:

https://doi.org/10.62672/joease.v3i3.118

Keywords:

Artificial intelligence, Electric fields, Learning styles, Multimodal learning, Pre-service teachers

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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Published

01-09-2025

How to Cite

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

Issue

Vol. 3 No. 3 (2025)

Section

Articles

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