Mike E. U. Ligthart

Elena Malnatsky, Shenghui Wang, Koen V. Hindriks et al.

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.

Mike E. U. Ligthart, Mark A. Neerincx, Koen V. Hindriks

We present a discovery-based, first version, explicit model of social interaction that provides a basis for measuring the quality of interaction of a human user with a social robot. The two core elements of the social interaction model are engagement and co-regulation. Engagement emphasizes the \textit{qualitative nature} of social interaction and the fact that a user needs to be drawn into the interaction with the robot. Co-regulation emphasizes the interaction process and the fact that a user and a robot need to be acting together. We argue that the quality of social interaction with a robot can be measured in terms of how efficiently engagement and co-regulation are established and maintained during the interaction and how satisfied the user is with the interaction.