Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912283945336832 |
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| author | Malnatsky, Elena Wang, Shenghui Hindriks, Koen V. Ligthart, Mike E. U. |
| author_facet | Malnatsky, Elena Wang, Shenghui Hindriks, Koen V. Ligthart, Mike E. U. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation Malnatsky, Elena Wang, Shenghui Hindriks, Koen V. Ligthart, Mike E. U. Artificial Intelligence 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. |
| title | Dialogic Learning in Child-Robot Interaction: A Hybrid Approach to Personalized Educational Content Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15762 |