A Study on AI-Enabled Basic Education Reform in China under the “Double Reduction”: Key Tasks and Strategies
Bibliographic record
Abstract
Within the framework of the “Double Reduction” in China, this study systematically examines the ways in which artificial intelligence (AI) empowers the reform of basic education. This study centers on three core issues - policy objectives, key tasks, and strategies. Their aim is to evaluate how effectively AI contribute to the key objectives of reducing students’ homework burden and alleviating tutoring pressure. Drawing on national policy documents and other relevant materials, and employing the Latent Dirichlet Allocation (LDA) model, this study identifies six major themes: urban educational provision and regional disparities; holistic student development under policy guidance; advancement of teachers’ instructional competence; diversification of educational provision and engagement of social forces; multidisciplinary teaching and learning research supported by AI; and educational infrastructure development and resource assurance. Collectively, they illustrate the multifaceted ways in which AI contributes to the formation of new pedagogical paradigms and the advancement of systemic transformation in education. The study finds that the deep integration of AI into basic education continues to face significant challenges. Achieving the objectives of AI-empowered education necessitates coordinated advancement in four critical dimensions: Top-level Design, Teacher Development, Technological application, Educational resource allocation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".