A Study on AI-Enabled Basic Education Reform in China under the “Double Reduction”: Key Tasks and Strategies

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.