The Impact of Artificial Intelligence on the Efficiency of Higher Education Resource Allocation

Abstract The rapid development of artificial intelligence (AI) is driving a systematic transformation of higher education paradigms and has gradually become a key force in optimizing higher education resource allocation. Based on panel data from 30 Chinese provinces during 2014–2023, this study employs Data Envelopment Analysis (DEA) to measure higher education resource allocation efficiency and uses two-way fixed effects models, mediation models, and threshold regression models to systematically examine the impact of AI on this efficiency. The findings are as follows. First, China's higher education resource allocation efficiency has improved overall, but significant regional heterogeneity persists—the eastern and central regions exhibit higher efficiency, while the western region remains generally low with declining trends in some provinces. Second, AI has a significantly positive effect on allocation efficiency, with the strongest impact in the western region and an insignificant effect in the central region. Third, AI improves higher education resource allocation efficiency by promoting regional economic development. Fourth, population density and regional innovation level each exhibit a single threshold effect: AI's positive effect diminishes when population density exceeds the threshold, and its impact shifts from negative to positive when regional innovation level crosses the threshold. These findings provide empirical evidence for leveraging AI to enhance higher education resource allocation efficiency and offer policy implications for the digital transformation of higher education in China.