This paper explores the potential of artificial intelligence (AI) in reshaping the paradigm of higher education teaching management, with a focus on constructing dynamic assessment systems and resolving multi-objective optimization challenges. Traditional management models face structural contradictions such as inefficient resource allocation, rigid standards, and fragmented data governance. By integrating AI with systems theory, decision theory, and educational psychology, this research proposes a paradigm shift toward data-driven governance. Core innovations include a multidimensional assessment framework incorporating real-time data and AI-powered personalized learning pathways. Empirical analysis demonstrates that AI-enabled digital twin systems can reconcile the tension between standardization and personalization while achieving proactive risk prediction. The findings validate AI’s role in supporting continuous process intervention and predicting academic risks, providing evidence for enhancing institutional teaching management efficacy and optimizing personalized student development trajectories through AI technologies.