Practical Exploration and Optimization Path of AI-empowered Personalized Mathematics Teaching in Higher Vocational Colleges

Abstract

Based on the Chaoxing AI Workbench, this paper targets the long-standing structural bottlenecks in higher vocational mathematics teaching, including diversified student demands, crude learning diagnosis, and insufficient teaching resources. A “Four-dimensional Linkage” enabling mechanism is constructed, which realizes precise stratification through student learning portrait diagnosis, solves the problem of homogeneous resource supply via stratified resource adaptation, runs through three classroom links with full-process teaching implementation, and guarantees personalized learning outcomes through dynamic diversified evaluation, forming a complete closed loop of “Diagnosis — Resource Supply — Teaching Implementation — Evaluation.” Practice results show that this mechanism achieves remarkable effects in improving teaching quality, narrowing gaps between students and optimizing learning experience. Aiming at practical shortcomings such as insufficient resource reserves, insufficient teachers’ digital literacy, poor technical adaptability and imperfect institutional guarantees, this paper further puts forward optimization countermeasures including inter-collegial co-construction and sharing, three-dimensional teacher transformation driving, collaborative tackling of technical defects and perfecting supporting systems, so as to provide useful reference for the intelligent transformation of public basic courses.