Pathways and Empirical Research on Generative Artificial Intelligence Empowering Curriculum Reform in Higher Education: A Case Study of the 'Customer Relationship Management' Course

Abstract

Paradigm-breaking innovations in generative artificial intelligence (GAI) are revolutionizing the landscape of postgraduate education at an unprecedented rate.This work chooses a Chinese university's "Customer Relationship Management"course as a case and explores both channels and empirical implications of GAI-driven curriculum innovation. This study presents a 'Three-Tier Three-Driver' personalized teaching reform model that categorizes students into three tiers based on different career trajectories and leverages AI as a driver across three dimensions: teaching content, teaching methods, and assessment.Multiple AI tools and methods like AI teaching assistants, GPT models, knowledge graph construction, Q&A modules, and AI assessment modules are used by the course to design the entire curriculum anew, boost pedagogical practice, and reconstruct assessment schemes.Data were collected from a questionnaire survey with 500 students and were modeled using K-Means clustering, DBSCAN density-based clustering algorithms and random forest models. Results demonstrate significant differences in student behaviors and perceptions related to AI tool usage across the different clusters.Effective use of AI technologies was found to benefit student satisfaction and academic performance. Furthermore, there was a significant association found between AI use times and course satisfaction, however DBSCAN could not detect a group of extreme learners.The study concludes by compiling replicable evidence of AI-driven teaching innovation and discussing existing limitations (such as the limited sample scope) as well as possible directions for future work.The conclusion uncovers GAI-empowered teaching innovation as a good promise for teaching quality improvement and personalized learning promotion for postgraduate education.