DOI: 10.57237/j.edu.2026.04.001 ISSN: 2996-2048

AI-Enabled Teaching Reform of Database Principles and Technology

Shaonan Liu
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The rapid advancement of database technology has fundamentally reshaped skill requirements across industries such as finance, e-commerce, and intelligent manufacturing. However, traditional teaching approaches to database courses in business-oriented programs face persistent challenges, including the cognitive gap between abstract theoretical concepts and real-world business scenarios, limited laboratory exercises disconnected from industry demands, and inefficient conventional teaching evaluation mechanisms. This study proposes and implements a comprehensive AI-enabled teaching reform framework integrating three core modules: (1) an AI-driven knowledge graph system for theoretical teaching, which constructs a structured knowledge corpus by integrating domain textbooks, accounting practice documents, and big data industry case repositories, with 186 database concept nodes, 94 application scenario nodes, and 412 semantic relationship edges to visualize complex concept relationships; (2) a generative AI-powered virtual business scenario platform with an intelligent SQL assessment engine, featuring a multi-modal generative model trained on domain-specific knowledge bases to produce 12 distinct synthetic business datasets with complete business logic chains and statistical consistency, along with a dual-path detection mechanism combining static syntax tree analysis and dynamic execution result evaluation for SQL assessment; and (3) a multimodal data-driven teaching evaluation and feedback mechanism, establishing a data integration pipeline connecting virtual scenario platform logs, classroom response system interaction data, and online assessment results, employing ensemble learning algorithms and clustering analysis to construct student ability profiles and identify collective learning patterns.

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