  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.
