Construction of a Process-based Evaluation System for Graduate Courses Driven by Industrial Engineering and Artificial Intelligence

Abstract

Against the global backdrop of AI-integrated pedagogy, this paper develops an intelligent graduate course management framework by synthesizing Industrial Engineering (IE) optimization with AI technologies. To address passive classroom engagement and fragmented exam preparation, the study employs Lark Minutes for automated interaction quantification and an Ordinary Least Squares (OLS) model for performance prediction. This enables a tiered early-warning system and AI-driven personalized interventions. Results demonstrate that the model significantly enhances participation and provides precision academic support, offering a scalable pathway for higher education’s digital transformation.