Mapping the learning styles of pre-service enviromental science education in interaction with artificial intelligence on the topic of electric fields - Amrullah, Jadnika Dwi Rakhmawan; Ahmad, Nur; Shilla, Rhischa Assabet (Journal of Environment and Sustainability Education, 2025)
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.