Optimizing Physics Teaching Through Artificial Intelligence: An Approach Adapted to Kolb's Divergent Learners

Abstract

This article explores the integration of artificial intelligence (AI) in high school physics teaching, based on Kolb's learning model. The study aims to personalize learning according to students' individual preferences and learning styles, while improving educational effectiveness through the optimization of teaching methods and learning outcomes. In addition, it seeks to create immersive learning environments through technological tools that promote active, engaging, and interactive experimentation. To achieve these objectives, a literature review was conducted to establish a theoretical framework on AI in education and its application in science. This phase was followed by the development and implementation of AI tools in real classrooms, with a qualitative and quantitative evaluation of the results that show an increase in student motivation and engagement, an improvement in academic performance, as well as positive feedback from teachers, who noted a better management of the various levels of learning. Finally, the study highlights the transformative potential of AI in science education, particularly in physics, while emphasizing the importance of thoughtful implementation to maximize its benefits.