Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
This work addresses the problem of ensuring age-appropriate and safe educational content in child-robot interactions, but it appears incremental as it builds on existing methods with a hybrid design.
The paper tackled the challenge of integrating foundational models into child-robot interactions for personalized educational dialogues by introducing a hybrid approach combining rule-based systems with LLMs for offline content generation and human validation, resulting in a framework applied to enhance reading motivation through robot-facilitated book-related dialogues.
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.