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Pedagogical agent research has yielded fruitful results in both academic skill learning and meta-cognitive skill acquisition, often studied in instructional or peer-to-peer paradigms. In the past decades, child-centric pedagogical research, which emphasizes the learner's active participation in learning with self-motivation, curiosity, and exploration, has attracted scholarly attention. Studies show that combining child-driven pedagogy with appropriate adult guidance leads to efficient learning and a strengthened feeling of self-efficacy. However, research on using social robots for guidance in child-driven learning still remains open and under-explored. In our study, we focus on children's exploration as the vehicle in literacy learning and develop a social robot companion that provides guidance to encourage and motivate children to explore during a storybook reading interaction. To investigate the effect of the robot's explorative guidance, we compare it against a control condition in which children have full autonomy to explore and read the storybooks. We conduct a between-subjects study with 31 children aged 4 to 6, and the result shows that children who receive explorative guidance from the social robot exhibit a growing trend of self-exploration. Further, children's self-exploration in the explorative guidance condition is found correlated to their learning outcome. We conclude the study with recommendations for designing social agents to guide children's exploration and future research directions in child-centric AI-assisted pedagogy.
Zhang et al. (Thu,) studied this question.