From Getting Acquainted to Reflective Discussion: A Multi-Session Architecture for Dialogic Educational Child-Robot Interaction

Abstract

Social robots are increasingly proposed as educational companions for children, but meaningful educational dialogue depends on something many systems leave undesigned: knowing the child, and sustaining that knowledge across multiple encounters. We formalize Dialogic Educational Child-Robot Interaction (DECRI), a child-centered framework where pedagogical intent and human expertise define the interaction and large language models are used only where they add clear value. We extend DECRI into a multi-session interaction architecture: early sessions elicit a child's user model, and later sessions draw on that accumulated knowledge, justifying a personalized learning material choice, building continuity, and supporting reflective discussion, so that personalization is earned through interaction. Content stays safe and traceable through a hybrid offline authoring-and-validation pipeline: the robot delivers only pre-validated utterances within a rule-based dialogic scaffold, with no generative model accessed at runtime. We examine the framework in Robot Bookworm, a real-school deployment with 51 children (aged 8–11) discussing books with a NAO robot across four sessions, drawing on content-quality metrics and child self-reports. Results show strong educational grounding, no unsafe content, and evidence consistent with the intended relational and cross-session experience, alongside clear limits: personalization remained bounded and affective content, especially humor, was difficult to assure. We argue that for embodied educational CRI the central design problem is not maximizing generative flexibility but architecting interaction that earns and uses what it learns about the child.