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

arXiv: https://arxiv.org/abs/2406.10934 · HTML: https://mlsanity.com/paper/2406.10934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design](https://mlsanity.com/paper/2507.16307/) (2025)
- [ChatGPT and Gemini participated in the Korean College Scholastic Ability Test -- Earth Science I](https://mlsanity.com/paper/2512.15298/) (2025)

## Abstract (arXiv)

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.