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

TL;DR

This paper explores how AI, specifically LLMs, can support family learning by mediating collaboration, balancing responsibilities, and enhancing shared educational experiences in everyday routines.

Contribution

It introduces FamLearn, an LLM-powered prototype designed to facilitate family collaboration in learning, addressing coordination and recognition challenges identified in a formative study.

Findings

FamLearn reduces caregiving burdens

It fosters recognition of contributions among family members

Enhances shared learning experiences

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.