Abstract: Digital technologies provide new pathways for the transformation of university teaching evaluation systems. Addressing issues such as the simplistic content, delayed feedback, and insufficient data support in traditional evaluation methods, this study constructs a“human-centered + data-driven”digital evaluation framework. By employing multimodal data collection and AI analytics for precise monitoring, it innovates a“human-machine collaborative”evaluation mechanism and establishes a full-cycle closed-loop optimization system. Scientific weight allocation ensures evaluation accuracy, driving a paradigm shift in assessment approaches and advancing educational evaluation upgrades, thereby laying a solid foundation for cultivating high-quality talents.

Key words: Artificial intelligence, University teachers, Teaching evaluation, New models, Talent cultivation, Evaluation system