AI-Enabled Teaching Reform of Database Principles and Technology
Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China
| Abstract: | 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. |
| Keywords: | AI in Education; Knowledge Graph; Generative AI; Database Curriculum Reform; Intelligent Assessment; Data-driven Teaching Evaluation |
| DOI: | 10.57237/j.edu.2026.04.001 |
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收稿日期: 2026-05-27
接受日期: 2026-06-04
在线出版日期: 2026-06-15
参考文献
| [1] | Chaudhuri, S., Dayal, U., Narasayya, V. An overview of business intelligence technology. Communications of the ACM. 2011, 54(8), 88–98. https://doi.org/10.1145/1978542.1978562 |
| [2] | Ben-Ari, M. Situated learning in computer science education. Computer Science Education. 2004, 14(2), 85-100. https://doi.org/10.1080/08993400412331363823 |
| [3] | Herrington, J., Oliver, R., Reeves, T. C. Patterns of engagement in authentic online learning environments. Australian Journal of Educational Technology. 2003, 19(1). https://doi.org/10.14742/ajet.1701 |
| [4] | Gibbs, G., Simpson, C. Conditions under which assessment supports students' learning. Learning and Teaching in Higher Education. 2005, 3–31. https://eprints.glos.ac.uk/id/eprint/3609 |
| [5] | Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education. 2019, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0 |
| [6] | Ocaña-Fernández, A., Valenzuela-Fernández, L. A., Garro-Aburto, L. L. Inteligencia artificial y sus implicaciones en la educación superior. Propósitos y Representaciones. 2019, 7(2), 536–568. |
| [7] | Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., De Melo, G., Gutierrez, C., et al. Knowledge graphs. ACM Computing Surveys. 2021, 54(4), 1–37. https://doi.org/10.1145/3447772 |
| [8] | Li, S., Xiao, Y. Automatic Construction System for Curriculum Knowledge Graphs Based on Large Language Models LLM-Based Curriculum Map Construction. In Proceedings of the 2025 2nd International Conference on Artificial Intelligence and Future Education. 2025, 252–257. https://doi.org/10.1145/3785987.3786028 |
| [9] | Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences. 2023, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 |
| [10] | López-Fernández, D., Vergaz, R. ChatGPT in Computer Science Education: A Case Study on a Database Administration Course. Applied Sciences. 2025, 15(2), 985. https://doi.org/10.3390/app15020985 |
| [11] | Tisha, S. M. Implications of Large Language Models in Database Management System Course. In Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2. 2026, 1549-1550. https://doi.org/10.1145/3770761.3777230 |
| [12] | Romero, C., Ventura, S. Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020, 10(3), e1355. https://doi.org/10.1002/widm.1355 |
| [13] | Zhang, X. Teaching Tip: Incorporating AI Tools Into Database Classes. Journal of Information Systems Education. 2025, 36(1), 37–52. https://doi.org/10.62273/GKZI2477 |
| [14] | Gaitantzi, A., Kazanidis, I. The Role of Artificial Intelligence in Computer Science Education: A Systematic Review with a Focus on Database Instruction. Applied Sciences. 2025, 15(7), 3960. https://doi.org/10.3390/app15073960 |
| [15] | Kubovčík, M., Luptáková, I. D., Pospíchal, J. AI-Driven Evaluation of SQL Tests: A Generative Large Language Model Approach. In 2025 International Conference on Emerging eLearning Technologies and Applications (ICETA). 2025, 495-499. IEEE. https://doi.org/10.1109/ICETA67772.2025.11280179 |
| [16] | Liu, X., et al. Synergizing Knowledge Graphs and LLMs: An Intelligent Tutoring Model for Self-Directed Learning. Education Sciences. 2025, 15(9), 1102. https://doi.org/10.3390/educsci15091102 |
| [17] | Zhu, Y., et al. Research on an intelligent tutoring system based on automatic construction of multimodal knowledge graphs and retrieval-augmented generation. Frontiers in Computer Science. 2026, 8, 1777749. https://doi.org/10.3389/fcomp.2026.1777749 |
| [18] | Arslan, A. K. Enhancing Educational QA Systems: Integrating Knowledge Graphs and Large Language Models for Context-Aware Learning. 2025. http://dx.doi.org/10.2139/ssrn.5104633 |
| [19] | Arias, M., et al. A Generative AI Tool to Foster and Assess Authentic Learning: A Case Study in Teaching SQL. Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education. 2025, 465–471. https://doi.org/10.1145/3724363.3729022 |
| [20] | Daly, C., Waldron, J. Assessing the assessment of programming ability. ACM SIGCSE Bulletin. 2004, 36(1), 210-213. https://doi.org/10.1145/1028174.971375 |
| [21] | Wang, Y., et al. Research on the Application of Generative Artificial Intelligence in Database Course Teaching. Vocational Education. 2025, 14(5), 45–51. https://doi.org/10.12677/ve.2025.145193 |
| [22] | Alrabah, A., Alawini, A. CodeLens: A Generative AI Framework for Automated Feedback on SQL Assignments. In Proceedings of the 4th International Workshop on Data Systems Education: Bridging Education Practice with Education Research. 2025, 29-34. https://doi.org/10.1145/3735091.3737534 |
| [23] | Dintyala, P., Narechania, A., Arulraj, J. (2020). SQLCheck: Automated detection and diagnosis of SQL anti-patterns. In Proceedings of the 2020 ACM SIGMOD international conference on management of data. 2020, 2331-2345. https://doi.org/10.1145/3318464.3389754 |
| [24] | Braun, V., Clarke, V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa |
| [25] | Aristeidou, M., Herodotou, C. Online citizen science: A systematic rev iew of effects on learning and scientific literacy. Citizen Science: Theory and Practice. 2020, 5(1), 1-12. https://doi.org/10.5334/cstp.224 |
| [26] | Sweller, J. Cognitive load during problem solving: Effects on learning. Cognitive Science. 1988, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 |
| [27] | Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355 |
| [28] | Black, P., Wiliam, D. Assessment and classroom learning. Assessment in Education. 1998, 5(1), 7–71. https://doi.org/10.1080/0969595980050102 |
