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Construction and Practice of a “Three-Stage Progressive” Teaching Model for a Digital Marketing Course Empowered by Generative Artificial Intelligence

Author(s)

Ruoqian Yang*

Affiliation(s)

School of Economics and Management, Zhongshan Polytechnic, Zhongshan, Guangdong, China *Corresponding Author

Abstract

To address the prevalent challenges in digital marketing courses at higher vocational colleges—namely rigid instructional models, disconnection from industrial practice, and monolithic evaluation systems—this study constructs a “Three-Stage Progressive” teaching model with Generative Artificial Intelligence (AIGC) as its core technological engine. The model is structured into three spiraling phases: Intelligent Adaptation, Physical-Virtual Integration, and Dynamic Assessment. It follows the intrinsic logic of data-driven processes, human-machine collaboration, and closed-loop optimization. During implementation, AIGC is used to generate personalized teaching resources and to plan adaptive learning pathways. A high-fidelity virtual simulation platform creates an immersive practicum that integrates the enterprise, market, and consumer. Furthermore, multi modal learning behavior analysis is applied to build a dynamic, process-oriented competency profiling system. Practical application indicates that this model significantly enhances students’ knowledge internalization, complex problem-solving skills, and digital strategic thinking. The study provides a replaceable reference for the reform of business education in the age of artificial intelligence.

Keywords

Generative Artificial Intelligence; Digital Marketing; Teaching Model; Three-Stage Progression; Physical-Virtual Integration; Dynamic Assessment

References

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