Beyond Answers: Large Language Model-Powered Tutoring System in Physics Education for Deep Learning and Precise Understanding

Abstract

The integration of artificial intelligence (AI) in education has shown significant promise, yet the effective personalization of learning, particularly in physics education, remains a challenge. This paper proposes Physics-STAR, a framework for large language model (LLM)- powered tutoring system designed to address this gap by providing personalized and adaptive learning experiences for high school students. Our study evaluates Physics-STAR against traditional teacher-led lectures and generic LLM tutoring through a controlled experiment with 12 high school sophomores. Results showed that Physics-STAR increased students' average scores and efficiency on conceptual, computational, and on informational questions. In particular, students' average scores on complex information problems increased by 100% and their efficiency increased by 5.95%. By facilitating step-by-step guidance and reflective learning, Physics-STAR helps students develop critical thinking skills and a robust comprehension of abstract concepts. The findings underscore the potential of AI-driven personalized tutoring systems to transform physics education. As LLM continues to advance, the future of student-centered AI in education looks promising, with the potential to significantly improve learning outcomes and efficiency.