AI-Enabled Online Learning in New Business Education: Pathways and Mechanisms for Whole-Process Precision Support

Abstract

At present, online learning in new business education still faces several challenges, including ambiguous learning status diagnosis, coarse-grained resource matching, delayed instructional feedback, and insufficient coordination among different teaching stages. Focusing on whole-process precision support, this study constructs four operational mechanisms: a dynamic closed-loop mechanism, a human–AI collaborative mechanism, a learning motivation mechanism, and a continuous optimization mechanism. Based on these mechanisms, an implementation pathway of “pre-class precise diagnosis- in-class personalized support- post-class continuous improvement” is proposed. By leveraging AI-based learner profiling, intelligent resource recommendation, dynamic feedback, and human–AI collaborative tutoring, this study enhances the ability of online teaching to identify and respond to individual differences among students. It promotes the transformation of new business education from standardized resource delivery toward personalized and whole-process learning services, providing theoretical and practical references for improving talent cultivation quality and advancing the intelligent transformation of business education.vvvvvvvvvvvvvvvvvvvvvv