AI Meets Academia: How Machine Learning is Transforming Higher Education in China

Abstract

This study investigates how machine learning (ML) is transforming higher education in China through pedagogical, institutional, and ethical dimensions. Using a convergent mixed-methods design, data were collected from 45 participants, including university administrators, lecturers, and students from leading institutions across Beijing, Shanghai, and Guangzhou. Quantitative data were analyzed using SPSS 27, yielding strong correlations between perceived usefulness (r = .69, p < .01), institutional support (r = .71, p < .01), and teaching effectiveness, while ethical concerns were negatively correlated (r = –.53, p < .01). Regression analysis (R² = .68, F (3,41) = 9.72, p < .001) identified institutional support (? = .47, p < .001) as the strongest predictor of ML’s perceived impact. Qualitative data analyzed with NVivo 12 revealed three major themes: enhanced teaching and research efficiency, data-driven decision-making, and ethical challenges including data privacy and algorithmic bias. Document analysis confirmed that national policies, such as the AI-in-Education Action Framework (2022), have accelerated ML integration but highlighted regional disparities and governance gaps. The study concludes that ML serves as a catalyst for innovation in Chinese higher education, fostering personalized learning, predictive analytics, and institutional modernization. However, its sustainability depends on human-centered governance, equitable access, and continuous faculty development. This research contributes to the growing body of evidence that ML’s success in academia is determined not merely by technological capability, but by ethical stewardship, institutional readiness, and inclusive implementation.

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