Physics Teaching with Artificial Intelligence (AI): A Personalized Approach for Accommodator-Style Learners According to Kolb

Abstract

This paper focuses on the integration of artificial intelligence (AI) applications in physics education, with the aim of improving the learning experience and performance of students identified as accommodative learners according to Kolb's model. We adopt a qualitative approach to integrate AI into the teaching and learning process, with an emphasis on pedagogical methods and the exploration of new perspectives. This work presents a case study on the evaluation of a physics assignment, involving a sample of 85 high school girls in science stream. To do this, we use tools such as questionnaires and interviews to collect information on the difficulties encountered during the assignment and in learning physics in general. Our results show that AI can offer personalized learning solutions, adapted to the specific needs and preferences of learners. We formulate concrete recommendations to optimize the use of AI, aimed at facilitating learning and developing students' skills. Finally, this approach highlights the importance of personalized pedagogy in physics teaching, opening the way to new perspectives to meet the unique needs of students.