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SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot
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AI-assisted learning companion robots are increasingly used in early education. Many parents express concerns about content appropriateness, while they also value how AI and robots could supplement their limited skill, time, and energy to support their children's learning. We designed a card-based kit, SET, to systematically capture scenarios that have different extents of parental involvement. We developed a prototype interface, PAiREd, with a learning companion robot to deliver LLM-generated educational content that can be reviewed and revised by parents. Parents can flexibly adjust their involvement in the activity by determining what they want the robot to help with. We conducted an in-home field study involving 20 families with children aged 3-5. Our work contributes to an empirical understanding of the level of support parents with different expectations may need from AI and robots and a prototype that demonstrates an innovative interaction paradigm for flexibly including parents in supporting their children.
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ParaTutor: Coordinating Parent and Child Math Tutoring through Role Separated LLM Scaffolding
Role-separated, phase-gated LLM scaffolding preserves parent-led math tutoring and children's reasoning better than generic LLM chat for 23 parent-child dyads.
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