  The integration of Large Language Models (LLMs) in education offers both
opportunities and challenges, particularly in fields like physics that demand
precise conceptual understanding. This study examines the capabilities of six
state-of-the-art LLMs in explaining the law of conservation of momentum, a
fundamental principle in physics. By analyzing responses to a consistent,
simple prompt in Japanese, we assess the models' explanatory approaches, depth
of understanding, and adaptability to different educational levels.Our
comprehensive analysis, encompassing text characteristics, response similarity,
and keyword usage, unveils significant diversity in explanatory styles across
models. ChatGPT4.0 and Coral provided more comprehensive and technically
detailed explanations, while Gemini models tended toward more intuitive
approaches. Key findings include variations in the treatment of critical
concepts such as net force, and differing emphases on mathematical rigor and
real-world applications.The results indicate that different AI models may be
more suitable for various educational contexts, ranging from introductory to
advanced levels. ChatGPT4.0 and Coral demonstrated potential for advanced
discussions, while Gemini models appeared more appropriate for introductory
explanations. Importantly, the study underscores the necessity of educator
guidance in effectively leveraging these AI tools, as models varied in their
ability to convey nuanced aspects of physical principles.This research
establishes a foundation for understanding the educational potential of LLMs in
physics, providing insights for educators on integrating these tools into their
teaching practices. It also highlights the need for further investigation into
AI-assisted learning in STEM fields, paving the way for more sophisticated
applications of AI in physics education.
