"Learning Together": AI-Mediated Support for Parental Involvement in Everyday Learning
Yao Li, Jingyi Xie, Ya-Fang Lin, He Zhang, Ge Wang, Gaojian Huang, Rui Yu, Si Chen
TL;DR
This paper addresses how AI can mediate family learning, shifting focus from child-centric tutoring to family-centered collaboration. It introduces FamLearn (ParPal in places), an LLM-powered prototype that decomposes and distributes learning tasks among caregivers, visualizes contributions via a shared timesheet, and provides tutoring-oriented support. A formative study identifies four tensions: cross-caregiver task division, limited collaboration, imbalanced labor, and parental gatekeeping; a 1-week field study with 11 families demonstrates reductions in caregiver cognitive burden, enhanced recognition of hidden labor, and richer family learning experiences, while also revealing constraints in capturing incidental learning and ensuring fair measurement of effort. The work contributes a family-centric design perspective for AI in learning, defines adaptable AI roles (Advisor/Co-Pilot/Guardian), and highlights practical implications for distributing responsibility, supporting intergenerational participation, and sustaining collaborative family learning in home environments.
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.