Research on AI-Driven Hierarchical Teaching Mode: A Case Study of Database Principles Course

To address the issue of unsatisfactory teaching effectiveness caused by the “one-size-fits-all” approach in traditional Database Principles course teaching, this paper proposes an AI-driven hierarchical teaching mode. By constructing a hierarchical teaching resource recommendation system based on student ability models and combining real-time learning situation analysis, complex teaching contents such as SQL practical operations and relational normalization theory are dynamically adjusted in difficulty and explained in a differentiated manner. Relying on the case of an AI teaching assistant in a university, this paper elaborates on the design concept, implementation process, and technical realization methods of this mode. Experimental results indicate that this mode significantly enhances the learning effectiveness and enthusiasm of students at different levels, providing new ideas and practical references for the reform of university curriculum teaching.