Exploring the Educational Landscape of AI: Large Language Models' Approaches to Explaining Conservation of Momentum in Physics

TL;DR

This study evaluates six advanced Large Language Models' ability to explain the conservation of momentum in physics, revealing diverse explanatory styles and implications for educational use across different learning levels.

Contribution

It provides a comparative analysis of LLMs' explanatory approaches in physics education, highlighting their strengths, limitations, and suitability for various educational contexts.

Findings

ChatGPT4.0 and Coral offer detailed, technical explanations.

Gemini models tend to give more intuitive, introductory explanations.

Models vary significantly in handling key physics concepts.

Abstract

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.