2025/03/20 by Elena Malnatsky, Shenghui Wang, Malnatsky, Elena +5
Computer Science · Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Educational Tools and Methods #FOS: Computer and information sciences #Innovative Teaching and Learning Methods #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2503.15762
openalex publication_date 2025/03/20 · openalex created_date 2025/10/17 · openalex updated_date 2026/07/28
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.