Enhancing Conceptual Understanding in Magnetism through AI-Powered Tools: A Mixed-Methods Study with High School Students

Kiani, Somayeh; Amini, Anahita

doi:10.48310/esip.2025.19823.1017

Enhancing Conceptual Understanding in Magnetism through AI-Powered Tools: A Mixed-Methods Study with High School Students

Document Type : Original Paper

Authors

Department of physics Education, Farhangian university, P. o. Box 14665-889, Tehran, Iran.

10.48310/esip.2025.19823.1017

Abstract

This research examines the influence of artificial intelligence (AI)-enabled instruments on comprehension, engagement, and retention of knowledge within high school physics education, particularly emphasizing the topic of magnetism. A mixed-methods methodology was utilized, merging a validated questionnaire distributed to 100 eleventh-grade pupils with qualitative analyses of open-ended responses alongside the practical application of selected AI instruments. The intervention comprised the employment of AI chatbots (e.g., ChatGPT), interactive simulations (PhET, Mozaik), concept mapping (Mindomo), and AI-generated educational music (Suno.ai). Quantitative findings demonstrated a significant consensus (78–85%) among students regarding perceived enhancements in understanding and engagement. Qualitative assessment indicated that chatbots and simulations were especially efficacious in elucidating misconceptions and facilitating the visualization of abstract concepts. A theoretical framework grounded in cognitive load theory and principles of multimedia learning is incorporated to elucidate the findings. Notwithstanding limitations pertaining to generalizability and access to technology, the research posits that a deliberate integration of AI tools can augment student-centered learning within the domain of physics. Suggestions for educators and avenues for future research are elaborated upon.

Keywords

Main Subjects

Teacher Education

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