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.

翻译：人工智能（AI）在教育领域的整合已展现出显著潜力，然而，特别是在物理教育中，实现有效的个性化学习仍面临挑战。本文提出Physics-STAR，一个基于大语言模型（LLM）的辅导系统框架，旨在通过为高中生提供个性化和自适应的学习体验来弥补这一不足。本研究通过一项包含12名高中二年级学生的对照实验，将Physics-STAR与传统教师主导的讲座及通用LLM辅导进行了比较评估。结果表明，Physics-STAR提高了学生在概念性、计算性和信息性问题上的平均得分和解题效率。特别是，学生在复杂信息问题上的平均得分提升了100%，效率提高了5.95%。通过提供逐步引导和促进反思性学习，Physics-STAR帮助学生发展批判性思维技能，并建立对抽象概念的扎实理解。这些发现强调了AI驱动的个性化辅导系统变革物理教育的潜力。随着LLM技术的持续进步，以学生为中心的AI在教育领域的未来前景广阔，有望显著提升学习效果与效率。