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Structuring the AI-enabled home learning environment: a gatekeeper model of digital capital, trust, and relational
XiaCheng Song1, Huafeng Qu1, Lu Sun2
1School of Information and Intelligent Engineering, Yunnan College of Business Management, Kunming, China.
Frontiers in Psychology
|July 13, 2026
Summary
Parents
Area of Science:
- Family Studies
- Educational Technology
- Artificial Intelligence Ethics
Background:
- AI tools are increasingly integrated into family life, positioning parents as crucial gatekeepers.
- Understanding parental factors influencing AI adoption in home learning is essential for developing governable AI ecologies.
Purpose of the Study:
- To examine the associations between family background, digital capital, AI beliefs, and relational support with parental behavioral intention and willingness to pay for AI in home learning.
- To investigate how socioeconomic status, digital literacy, AI trust, and privacy concerns influence parental engagement with AI-enabled learning environments.
Main Methods:
- A survey of 585 Chinese parents of children aged preschool to secondary school was conducted, with 567 valid responses analyzed.
- An associational structural path model was used to link various parental and household factors to behavioral intention and willingness to pay for AI in education.
Main Results:
- Household AI use positively correlated with parental digital literacy, which in turn was linked to higher AI trust and behavioral intention.
- Socioeconomic status influenced downstream support mainly through parental digital literacy.
- Parental behavioral intention was more strongly predicted by the model than willingness to pay, with similar patterns observed across different socioeconomic status groups.
Conclusions:
- Equity-focused AI in education initiatives should enhance parents' digital capabilities, foster calibrated trust, and build relational support systems.
- These infrastructures empower families to actively govern AI in children's learning, rather than passively consume it.
- Findings highlight associations, not causal pathways, due to the cross-sectional nature of the data.
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