An AI-driven modular digital marketing training platform with enhanced efficiency and privacy protection: system design and pedagogical validation

This paper aims to enhance the intelligent level and practical training effect of digital marketing teaching in colleges and universities, construct a practical training platform integrating artificial intelligence technology, and integrate the task configuration engine, user modeling module and lightweight AI algorithms (such as DistilBERT, LightGBM) to achieve efficient reasoning and personalized recommendation. The platform adopts a hierarchical modular architecture and introduces federated learning and differential privacy mechanisms to ensure the security and compliance of user behavior data. In the two sets of controlled experiments, the AI platform significantly outperformed the traditional platform in terms of task configuration flexibility, model response performance, and data fusion quality, verifying its advantages in teaching adaptability and technical feasibility. The research provides theoretical support and practical paradigms for the deep integration of AI in the education system.