Artificial Intelligence (AI) technology is driving a profound educational transformation, shifting from a supplementary instructional tool toward a strategic enabler of teaching management and instructional coordination. In traditional educational settings, teachers shoulder extensive responsibilities, which creates growing challenges in balancing instructional quality with increasing managerial and administrative demands. A critical issue is how AI can be leveraged to reduce teaching management burdens while improving the efficiency and coordination of instructional processes. To address this issue, this study investigates an AI-enabled multi-agent collaborative approach to support teaching management across the instructional workflow. The coordinated operation of multiple pedagogical AI teaching assistant supports core management functions, including lesson planning, assessment design, grading, learning progress monitoring, and instructional feedback coordination. The study is based on a real-world pilot implementation in a university setting and adopts a descriptive evaluation framework drawing on system usage data and faculty and student feedback. Results from nearly one year of application indicate that AI-supported teaching management can reduce lesson preparation time by approximately 70%, improve grading efficiency by about 90%, and enable rapid generation of multiple exam paper versions. These findings suggest that AI-enabled multi-agent collaboration can serve as an effective support mechanism for more efficient and coordinated teaching management in higher education.
DU et al. (Sun,) studied this question.
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