用规则+大模型生成适合孩子的个性化阅读对话
Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
- 结合规则系统与大模型，分步生成教育对话内容
- 通过人工审核确保内容符合儿童发展与教学目标
- 适合教育机器人、儿童互动系统研发者参考
对话式学习通过有目的、结构化的对话促进教育中的动机和深度理解。基础模型为儿童-机器人互动提供了变革性潜力，可实现个性化、吸引人且可扩展的交互设计。然而，将其融入教育场景面临内容适龄性、安全性及教学目标对齐的挑战。本文提出一种混合方法，用于设计儿童-机器人互动中的个性化教育对话。通过将基于规则的系统与大模型结合，进行选择性离线内容生成，并经人工验证，确保教育质量与发展适宜性。我们以提升阅读动机为目标，开展项目实践，让机器人引导与书籍相关的对话。
原文摘要 · Abstract (English)
Dialogic learning fosters motivation and deeper understanding in education through purposeful and structured dialogues. Foundational models offer a transformative potential for child-robot interactions, enabling the design of personalized, engaging, and scalable interactions. However, their integration into educational contexts presents challenges in terms of ensuring age-appropriate and safe content and alignment with pedagogical goals. We introduce a hybrid approach to designing personalized educational dialogues in child-robot interactions. By combining rule-based systems with LLMs for selective offline content generation and human validation, the framework ensures educational quality and developmental appropriateness. We illustrate this approach through a project aimed at enhancing reading motivation, in which a robot facilitated book-related dialogues.
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