Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
- URL: http://arxiv.org/abs/2503.15762v1
- Date: Thu, 20 Mar 2025 00:46:10 GMT
- Title: Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
- Authors: Elena Malnatsky, Shenghui Wang, Koen V. Hindriks, Mike E. U. Ligthart,
- Abstract summary: Dialogic learning fosters motivation and deeper understanding in education through purposeful and structured dialogues.<n>Foundational models offer a transformative potential for child-robot interactions, enabling the design of personalized, engaging, and scalable interactions.<n>We introduce a hybrid approach to designing personalized educational dialogues in child-robot interactions.
- Score: 1.5574423250822542
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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