A PCA-K-Means-Random Forest Framework for Analyzing AI-Enabled Educational Governance in Higher Education

Abstract

With the increasing integration of artificial intelligence (AI) into higher education, its role in supporting educational governance and management has attracted growing scholarly attention. However, existing studies often focus on technological applications while lacking systematic quantitative analyses of stakeholder heterogeneity and key driving factors. Against this background, this study aims to explore the latent structure, group differences, and critical determinants of AI-enabled educational governance using a data-driven approach. Based on questionnaire survey data, Principal Component Analysis (PCA) is employed to reduce the dimensionality of high-dimensional perceptual variables, K-Means clustering is applied to identify heterogeneous stakeholder groups with distinct perceptions and attitudes toward AI-enabled management, and a Random Forest model is further used to examine the key factors influencing support for AI investment. The results indicate substantial heterogeneity across stakeholder groups, and reveal that perceived improvements in management efficiency, enhanced fairness, and acceptance of AI-assisted decision-making are the primary drivers of support for continued AI investment. By integrating dimensionality reduction, clustering, and key driver identification, this study proposes a machine learning–based analytical framework that extends empirical research on AI-enabled educational governance and provides data-driven insights for differentiated governance strategies across multiple application scenarios.

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