Research on the Impact Mechanism of Artificial Intelligence-Driven Precise Allocation of Educational Resources on Social Stratification Mobility: Research and Regulation

This study investigates the pathways and mechanisms through which artificial intelligence (Al) technology influences social stratification mobility by optimizing the allocation of educational resources. Based on provincial panel data from China (2015-2022), we construct an "Al-Education Adaptation Index" and a quantitative social mobility model, integrating machine learning algorithms and structural equation modeling (SEM). Key findings include: (1) Al-driven precision resource allocation reduces the educational Gini coefficient by 12.3%, though the distribution of technological dividends exhibits regional heterogeneity; (2) Algorithmic fairness demonstrates a significant moderating effect (ß = 0.47, p < 0.01), with underdeveloped regions experiencing a 28% marginal gain; (3) Intergenerational occupational mobility increases by 19.6% through the mediating role of educational opportunities. The results suggest that Al can reshape social mobility structures via dual "efficiency-equity" pathways, yet algorithmic bias risks exacerbating Matthew effects. This research provides a theoretical foundation for educational policy design in the context of digital transformation.