Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation

TL;DR

This paper presents a hybrid system combining rule-based methods and large language models to generate personalized, safe, and pedagogically aligned educational dialogues for child-robot interactions, enhancing reading motivation.

Contribution

It introduces a novel hybrid framework integrating rule-based and LLM techniques with human validation for educational dialogue generation in child-robot interactions.

Findings

Effective personalization of dialogues for children.

Ensured age-appropriate and pedagogically aligned content.

Improved reading motivation through robot-facilitated dialogues.

Abstract

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.