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.

翻译：本研究探讨了日语敬语对大型语言模型（LLM）在解释动量守恒定律时生成回复的影响。我们分析了包括ChatGPT、Coral和Gemini的多种变体在内的六个前沿AI模型，使用了14种不同的敬语形式。研究发现，敬语显著影响AI生成回复的质量、一致性和正式程度，表明LLM具备理解和适应语言中社会语境线索的能力。不同模型间存在显著差异，部分模型侧重于历史背景和公式推导，而其他模型则聚焦于直观解释。本研究凸显了在教育场景中利用敬语调节AI生成解释的深度与复杂度的潜力。此外，AI模型对文化语言元素的响应能力，强调了在开发教育应用AI时考虑文化因素的重要性。这些结果为AI辅助教育和AI系统文化适应研究开辟了新途径，对个性化学习体验和开发面向全球教育的文化敏感性AI工具具有重要启示。