儿童-机器人交互中的对话式学习：一种个性化教育内容生成的混合方法
Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
AI总结：
针对基础模型融入儿童-机器人教育交互时的内容适龄安全、教学对齐难题，提出结合规则系统、LLM离线内容生成与人工验证的混合对话设计方法，并通过阅读动机提升项目验证其可行性。
摘要：
对话式学习通过有目的、结构化的对话在教育中激发学习动机并促进深度理解。基础模型为儿童-机器人交互带来变革性潜力，可支持个性化、高参与度且可扩展的交互设计。然而，将其整合到教育场景中时，在确保内容适龄安全、契合教学目标方面存在挑战。我们提出一种在儿童-机器人交互中设计个性化教育对话的混合方法。该框架将基于规则的系统与大语言模型（LLMs）结合，用于选择性离线内容生成，并加入人工验证，保障教育质量与发展适宜性。我们通过一个提升阅读动机的项目阐释该方法，项目中由机器人引导开展书籍相关对话。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
