Building AI Literacy at Home: How Families Navigate Children's Self-Directed Learning with AI

TL;DR

This study investigates how families navigate children's self-directed learning (SDL) with generative AI in middle childhood. Using formative work and focus-group data from Chinese families, it reveals that parents frame AI literacy as a phased process linked to screen-time management, self-directness, and knowledge growth, yet often treat AI as primarily a learning tool, with limited awareness of broader risks. The paper then outlines design implications for AI systems that scaffold SDL through gatekeeping, calibrated access, facilitation, and evaluative visibility, encouraging co-learning and adaptive parental involvement.its findings suggest practical pathways for developing family-centered AI tools that support children’s autonomy while maintaining guidance and safety. The work highlights the need for developmental roadmaps and dual-lens literacy to bridge pragmatic educational use with critical, socio-technical understanding in home learning ecosystems.

Abstract

As generative AI becomes embedded in children's learning spaces, families face new challenges in guiding its use. Middle childhood (ages 7-13) is a critical stage where children seek autonomy even as parental influence remains strong. Using self-directed learning (SDL) as a lens, we examine how parents perceive and support children's developing AI literacy through focus groups with 13 parent-child pairs. Parents described evolving phases of engagement driven by screen time, self-motivation, and growing knowledge. While many framed AI primarily as a study tool, few considered its non-educational roles or risks, such as privacy and infrastructural embedding. Parents also noted gaps in their own AI understanding, often turning to joint exploration and engagement as a form of co-learning. Our findings reveal how families co-construct children's AI literacy, exposing tensions between practical expectations and critical literacies, and provide design implications that foster SDL while balancing autonomy and oversight.

Figures (2)

• Figure 1: Staged family plans for children’s AI use. Each horizontal bar represents one family’s planning pathway; each five-pointed star locates the child’s current age. The left side shows Stage 1: family (parent and child) familiarity with AI in four groups. The right side maps subsequent stages along the age timeline: Stage 2 (light yellow-green) = parent and child use AI together for learning, Stage 3 (medium green) = child uses AI under parental supervision, and Stage 4 (dark green) = child uses AI independently. Dashed grids and school-level markers (Primary/Junior High/Senior High/College) align age anchors (e.g., 12, 15, 18). Diagonal hatching denotes periods with no clear plan, and light-gray shading indicates stages already completed. The visualization highlights both convergence, such as delaying independence until after primary school and clustering transitions around junior high, and divergence, as families differ widely in when and how they envision autonomy, with “independent” use sometimes still bounded by parental oversight. (The lines are not ordered by Household ID)
• Figure 2: Compact horizontal mapping: AI roles (top) and Parent roles (bottom) both support four SDL stages (middle).