The Homework Wars: Exploring Emotions, Behaviours, and Conflicts in Parent-Child Homework Interactions

September 3, 2025·
Nan Gao
,
Yibin Liu
,
Xin Tang
,
Yanyan Liu
,
Chun Yu
,
Yun Huang
Yuntao Wang
Yuntao Wang
,
Flora D. Salim
,
Xuhai Xu
,
Jun Wei
,
Yuanchun Shi
· 0 min read
Abstract
jats:pParental involvement in homework is a crucial aspect of family education, but it often triggers emotional strain and conflicts. Despite growing concern over its impact on family well-being, prior research has lacked access to fine-grained, real-time dynamics of these interactions. To bridge this gap, we present a framework that leverages naturalistic parent-child interaction data and large language models (LLMs) to analyse homework conversations at scale. In a four-week in situ study with 78 Chinese families, we collected 475 hours of audio recordings and accompanying daily surveys, capturing 602 homework sessions in everyday home settings. Our LLM-based pipeline reliably extracted and categorised parental behaviours and conflict patterns from transcribed conversations, achieving high agreement with expert annotations. The analysis revealed significant emotional shifts in parents before and after homework, 18 recurring parental behaviours and seven common conflict types, with Knowledge Conflict being the most frequent. Notably, even well-intentioned behaviours were significantly positively correlated with specific conflicts. This work advances ubiquitous computing methods for studying complex family dynamics and offers empirical insights to enrich family education theory and inform more effective parenting strategies and interventions in the future.</jats:p>
Type
Publication
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
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Yuntao Wang
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Associate Professor (Research Track)
Yuntao Wang’s research centers on physiobehavioral computing and intelligent interaction for mobile and wearable systems. His work focuses on (1) developing robust, efficient sensing that performs reliably on mainstream devices, (2) extracting spatiotemporal patterns from multimodal signals to infer interaction intent by leveraging natural behavioral correlations, and (3) designing edge-efficient interfaces that deliver high performance on mobile and wearable platforms. He has published 90+ papers, received 10 international conference awards, and holds 30+ granted patents. His contributions have been recognized with honors including the Wu Wenjun AI Outstanding Youth Award (2024), the CAST Young Elite Scientists Sponsorship Program (2022), the Qinghai High-Level Innovation & Entrepreneurship Leading Talent (2024), and the First Prize of the China Electronics Institute Science & Technology Award (2019).
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