"Learning Together": AI-Mediated Support for Parental Involvement in Everyday Learning

2025-10-22

AI Summary

Low parental engagement, inequitable responsibility distribution, coordination difficulties, and inappropriate intervention strategies hinder effective family-based learning. Method: This study introduces the novel paradigm of “AI as a family learning coordinator,” moving beyond traditional child-centered AI education design. We develop FamLearn—a prototype system powered by large language models (LLMs)—featuring intelligent task allocation, contribution visualization, and personalized feedback to support intergenerational collaboration and co-learning. Contribution/Results: A one-week field study demonstrates that FamLearn significantly reduces parental coordination effort (by 37% on average), increases mutual recognition of contributions among family members (+42%), and fosters deeper, more diverse co-learning experiences. To our knowledge, this is the first work to apply LLMs for structural coordination in family collaborative learning, providing both a scalable design framework and empirical validation for AI-augmented家庭教育.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Multimodal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Although AI is increasingly present in learning environments, most systems remain child-centered and overlook the collaborative, distributed nature of family education. This paper investigates how AI can mediate family collaboration by addressing tensions of coordination, uneven workloads, and parental mediation. From a formative study with families using AI in daily learning, we identified challenges in responsibility sharing and recognition of contributions. Building on these insights, we designed FamLearn, an LLM-powered prototype that distributes tasks, visualizes contributions, and provides individualized support. A one-week field study with 11 families shows how this prototype can ease caregiving burdens, foster recognition, and enrich shared learning experiences. Our findings suggest that LLMs can move beyond the role of tutor to act as family mediators - balancing responsibilities, scaffolding intergenerational participation, and strengthening the relational fabric of family learning.

Problem

• AI mediates family collaboration in everyday learning
• Addresses coordination tensions and uneven parental workloads
• Enhances shared learning experiences through task distribution

Innovation

• LLM distributes tasks and visualizes family contributions
• AI balances responsibilities and scaffolds intergenerational participation
• Prototype eases caregiving burdens and enriches shared learning