Division of Labor and Collaboration Between Parents in Family Education

Homework tutoring work is a demanding and often conflict-prone practice in family life, and parents often lack targeted support for managing its cognitive and emotional burdens. Through interviews with 18 parents of children in grades 1-3, we examine how homework-related labor is divided and coordinated between parents, and where AI might meaningfully intervene. We found three key insights: (1) Homework labor encompasses distinct dimensions: physical, cognitive, and emotional, with the latter two often remaining invisible. (2) We identified father-mother-child triadic dynamics in labor division, with children’s feedback as the primary factor shaping parental labor adjustments. (3) Building on prior HCI research, we propose an AI design that prioritizes relationship maintenance over task automation or broad labor mitigation. By employing labor as a lens that integrates care work, we explore the complexities of labor within family contexts, contributing to feminist and care-oriented HCI and to the development of context-sensitive coparenting practices.

💡 Research Summary

**
This paper investigates how homework tutoring—a routine yet demanding activity in early primary education—is divided and coordinated between parents, and how artificial intelligence (AI) might intervene to promote more equitable collaboration. Drawing on semi‑structured interviews with 18 Chinese parents of children in grades 1‑3 (12 mothers, 6 fathers), the authors adopt a “labor” lens that distinguishes three dimensions of work: physical (preparing materials, managing schedules), cognitive (planning, monitoring, problem‑solving), and emotional (soothing frustration, managing parental stress). The analysis reveals that while physical labor is visible, cognitive and emotional labor remain largely invisible and are disproportionately shouldered by mothers.

A key contribution is the identification of a triadic “father‑mother‑child” dynamic. Children’s immediate feedback—success, failure, or complaints—acts as a trigger that reshapes parental roles in a recurring “breakdown‑substitution‑repair” cycle. When a child struggles, mothers typically provide instant emotional support and cognitive scaffolding; fathers intervene later, often in a more episodic, supervisory capacity (e.g., checking completed work, offering encouragement). This cycle underscores that parental coordination is not static but continuously renegotiated based on the child’s affective signals.

The authors argue that existing AI‑supported educational tools focus on task automation or enhancing child learning outcomes, neglecting the relational and labor‑distribution aspects of homework tutoring. Instead, they propose an AI design agenda that prioritizes relationship maintenance. Three concrete design ideas are offered: (1) collaborative annotation tools that make each parent’s physical, cognitive, and emotional contributions visible and shareable; (2) child‑centered narrative timelines that map emotional states and learning progress over time, giving parents a shared “story” of the tutoring episode; and (3) joint reflection interfaces that prompt parents to discuss, renegotiate, and balance their responsibilities after each tutoring session. By foregrounding the hidden labor, these designs aim to surface gendered imbalances, facilitate transparent communication, and provide scaffolding at moments of breakdown.

Methodologically, the study combines a screening survey (133 respondents) with purposive sampling to ensure participants experience regular homework tutoring and have encountered conflict. Interviews are coded for labor type, coordination patterns, and attitudes toward AI. The sample reflects the Chinese context where mothers dominate tutoring (10 mother‑only households, 5 joint, 3 father‑only), confirming prior findings of gendered labor asymmetry.

Limitations include the cultural specificity to urban Chinese families, reliance on self‑reported data without direct observation, and the fact that AI design proposals remain conceptual without prototype evaluation. Future work should test these designs in diverse cultural settings, develop functional prototypes, and measure impacts on parental stress, gender equity, and child learning outcomes.

In sum, the paper advances HCI and CSCW scholarship by (1) applying a labor framework to the informal, affect‑laden domain of family education, (2) revealing a dynamic triadic coordination model driven by child feedback, and (3) reframing AI’s role from task automation to relational scaffolding. This perspective opens pathways for technology that not only supports learning but also mitigates hidden burdens and promotes more sustainable, equitable coparenting practices.