AI-Empowered Learning Ecology Under China’s ‘Double Reduction’ Policy

China’s 2021 “Double Reduction” policy reduces student academic burdens and regulates tutoring, yet challenges remain in equitable education access and student well-being. This study proposes a digital education model—the Health Learning Chain (HLC)—using biological-behavioral big data to optimize learning through physiological, psychological, and behavioral insights. Employing mixed methods (interviews, surveys, and system dynamics modeling), it explores multi-agent collaboration and resource sharing in sports-education integration. Key findings highlight stakeholder cooperation and resource optimization as vital for success. Policy simulations show short-term gains from increased resources but emphasize long-term reliance on governance and policy support. Recommendations include boosting resources, enhancing collaboration, and establishing sustainable policy frameworks, offering actionable strategies for a balanced educational ecosystem under “Double Reduction”.

Authors

Publication Details

Journal
Journal of Integrated Social Sciences and Humanities
Published
2025-04-18
DOI
https://doi.org/10.62836/jissh.v2i1.357
Primary Topic
Global Educational Reforms and Inequalities
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Empowered Learning Ecology Under China’s ‘Double Reduction’ Policy

Yiping Su, Zhiyao Su
Journal of Integrated Social Sciences and Humanities
Global Educational Reforms and Inequalities
article

AI-Empowered Learning Ecology Under China’s ‘Double Reduction’ Policy

Yiping Su, Zhiyao Su
article en

Abstract

China’s 2021 “Double Reduction” policy reduces student academic burdens and regulates tutoring, yet challenges remain in equitable education access and student well-being. This study proposes a digital education model—the Health Learning Chain (HLC)—using biological-behavioral big data to optimize learning through physiological, psychological, and behavioral insights. Employing mixed methods (interviews, surveys, and system dynamics modeling), it explores multi-agent collaboration and resource sharing in sports-education integration. Key findings highlight stakeholder cooperation and resource optimization as vital for success. Policy simulations show short-term gains from increased resources but emphasize long-term reliance on governance and policy support. Recommendations include boosting resources, enhancing collaboration, and establishing sustainable policy frameworks, offering actionable strategies for a balanced educational ecosystem under “Double Reduction”.

Journal of Integrated Social Sciences and Humanities
Openalex Percentile: Top 87%
Global Educational Reforms and Inequalities
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AI-Empowered Learning Ecology Under China’s ‘Double Reduction’ Policy — Yiping Su, Zhiyao Su · Journal of Integrated Social Sciences and Humanities (2025) | TGRS Research Map | TGRS