In the context of high-quality development of vocational education, traditional student evaluation models face practical challenges such as limited data dimensions, delayed feedback, and insufficient personalized support. The emergence of artificial intelligence (AI) technologies offers new pathways for constructing intelligent evaluation systems. This paper systematically reviews research advances in AI-enabled comprehensive quality evaluation of vocational students, particularly in higher vocational education. It begins by analyzing the evolution of evaluation systems from traditional point-based mechanisms to information platforms and then to intelligent evaluation systems, highlighting existing limitations in data integration depth, analytical capabilities, and intervention effectiveness. Furthermore, the paper elaborates on a technical framework centered on multi-source data fusion, dynamic behavior modeling, intelligent early warning, and cluster analysis. It critically examines challenges in the field, including data privacy and ethical regulation, algorithmic fairness and interpretability, and the deep integration of technology with educational principles. Finally, the study proposes an integrated “data-driven governance” framework and outlines future research directions from three dimensions: technological iteration, theoretical innovation, and policy support, aiming to provide theoretical foundations for advancing vocational education evaluation systems toward greater scientific rigor, precision, and intelligence.
Cite this paper
Lu, Q. , Zhang, Y. , Lin, Y. , He, J. and Feng, M. (2025). AI-Enabled Comprehensive Quality Assessment in Higher Vocational Education: A Review and Framework Proposal. Open Access Library Journal, 12, e14602. doi: http://dx.doi.org/10.4236/oalib.1114602.
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