SET-PAiREd: Designing for Parental Involvement in Learning with an AI-Assisted Educational Robot

2025-02-24

AI Summary

This study addresses the tension between parental authority and AI autonomy in AI-powered educational robots for children aged 3–5. Method: We propose the “adaptive parental co-regulation” paradigm, designing an AI educational robot system that dynamically modulates parental involvement. We employ Situation-Embedded Task (SET) modeling to characterize multidimensional participation contexts, develop the PAiREd human–AI interface enabling parents to review and revise LLM-generated content, and conduct a field evaluation across 20 families. Contribution/Results: This work provides the first empirical evidence of heterogeneous parental expectations regarding AI support. Results demonstrate significant improvements in parental trust (92% expressed willingness for long-term adoption) and perceived control; 76% of AI-generated content was revised by parents, effectively bridging the gap between technological empowerment and parental educational agency.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Designing AI-assisted educational robot for children
• Enabling flexible parental involvement in learning
• Developing interface for parent-reviewed educational content

Innovation

• AI-assisted educational robot
• Card-based kit design
• Parent-adjustable learning content