Artificial Intelligence Drives Optimization and Precision Governance of University Teaching Management Decisions

Abstract

Background: Artificial intelligence (AI) offers new opportunities to optimize teaching management and enable precision governance through predictive analytics, intelligent optimization, and decision-support systems.

Objective: This study aimed to examine how AI drives the optimization and precision governance of university teaching management decisions, focusing on decision efficiency, resource allocation, teaching quality responsiveness, differentiated governance, and transparency.

Methods: A mixed-methods research design was employed, integrating quantitative AI-based modeling with qualitative governance analysis. Multi-source institutional data, including teaching administration records, learning analytics, teaching evaluation data, and policy documents, were analyzed over a three-year period. Machine learning models were applied for prediction tasks, optimization algorithms were used for resource allocation, and natural language processing was employed to analyze qualitative feedback. Semi-structured interviews with teaching management personnel complemented quantitative findings.

Results: AI-driven teaching management significantly reduced decision cycle time and administrative workload, improved classroom utilization and faculty workload balance, and enhanced predictive accuracy for key governance outcomes. Teaching quality issues were identified more rapidly, corrective interventions increased, and overall teaching evaluation scores improved. Importantly, AI enabled differentiated, data-driven governance across departments and student subgroups while increasing decision transparency, consistency, and stakeholder trust.

Conclusion: Artificial intelligence substantially enhances the optimization and precision of university teaching management decisions. By supporting proactive, differentiated, and transparent governance, AI serves as a critical enabler of evidence-based and adaptive teaching management. Responsible implementation, supported by ethical safeguards and human-in-the-loop governance, is essential to fully realize its transformative potential in higher education.