Addressing the deep-seated structural contradiction between "large-scale education and personalized cultivation of top-notch innovative talents" amidst the continuous expansion of postgraduate education, and the existing bottlenecks in traditional postgraduate education such as a single evaluation dimension, lagging process feedback, and the separation of teaching and evaluation, this paper, based on educational theory and system mechanism design, constructs a personalized education theoretical model and an intelligent multidimensional teaching quality assessment theoretical model based on an AI-powered large-scale model. The personalized education theoretical model, by integrating multimodal data such as visual, audio, and text, establishes a six-dimensional quantitative indicator system covering behavioral patterns, academic progress, interest matching, learning progress, knowledge point mastery, and behavioral relevance. The intelligent multidimensional teaching quality assessment theoretical model, centered on three levels—"basic course quality, teaching behavior, and interactive feedback"—designs a multidimensional quantitative indicator system including blackboard writing standardization, lesson plan quality, semantic differences in abstracts, temporal behavioral transformation, and genuine teacher-student emotions. Based on this, this paper studies the two-way dynamic coupling mechanism between personalized education and multidimensional teaching quality assessment , proposes a multi-level support framework covering cloud service layer, AI large model layer, AI-driven functional service layer, data processing layer and platform system layer, and designs in depth the implementation path and institutional guarantee mechanism for advanced training, providing solid theoretical model support and systematic mechanism design for resolving the contradiction between large-scale postgraduate education and personalized training.
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Peng et al. (2026) studied this question.
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