  This study investigates the influence of Japanese honorifics on the responses
of large language models (LLMs) when explaining the law of conservation of
momentum. We analyzed the outputs of six state-of-the-art AI models, including
variations of ChatGPT, Coral, and Gemini, using 14 different honorific forms.
Our findings reveal that honorifics significantly affect the quality,
consistency, and formality of AI-generated responses, demonstrating LLMs'
ability to interpret and adapt to social context cues embedded in language.
Notable variations were observed across different models, with some emphasizing
historical context and derivations, while others focused on intuitive
explanations. The study highlights the potential for using honorifics to adjust
the depth and complexity of AI-generated explanations in educational contexts.
Furthermore, the responsiveness of AI models to cultural linguistic elements
underscores the importance of considering cultural factors in AI development
for educational applications. These results open new avenues for research in
AI-assisted education and cultural adaptation in AI systems, with significant
implications for personalizing learning experiences and developing culturally
sensitive AI tools for global education.
