Exploration and Practice of Reforming Experimental Teaching in Universities Empowered by Large Language Models

Case Study of the Integrated Virtual Simulation Training Course on Modern Enterprise Operations

Authors

  • Qin Zhang
  • Zhongqin Xie
  • Huimei Wang

DOI:

https://doi.org/10.54691/4x8kp856

Keywords:

Large language models, teacher-student-AI triadic collaboration, OBE, AI agents, experimental teaching.

Abstract

Based on the concept of OBE (Outcome-Based Education), this paper innovatively constructs an experimental teaching system for management students. Taking the integrated virtual simulation training course on modern enterprise operations as an example, it develops and implements a “four-stage progressive” experimental teaching system empowered by large language models from the perspective of teacher-student-AI triadic collaboration. Through full-chain reconstruction across “cognitive empowerment-design empowerment-implementation empowerment-evaluation empowerment,” large language models are deeply integrated into every stage of experimental teaching, forming a new teaching ecology characterized by intelligent guidance, dynamic generation, human-AI collaboration, and precise assessment. The model has been practiced in training courses offered across six undergraduate majors, including big data management and application, supply chain management, business administration, and logistics management. Teaching practice shows that the introduction of large language models has enabled a shift from knowledge transmission to intelligently enhanced competence cultivation, thereby improving students’ ability to adapt to the intelligent era.

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References

[1] Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224.

[2] Ministry of Education and Eight Other Departments. (2025, April 11). Opinions on accelerating the advancement of education digitalization [J/OL]. https://www.gov.cn/zhengce/zhengceku/202504/content_7019045.htm

[3] Luo, F., Ma, Y., & Jiao, L. Z. (2025). Iteration-driven transformation: How DeepSeek's technical characteristics empower digital transformation in education. Journal of Soochow University (Educational Science Edition), 13(3), 28–39.

[4] Zhang, L. N., Gu, R. T., & Wang, Q. Y. (2025). A study on the application of large language models in language experiment teaching. Experimental Technology and Management, 42(10), 201–209.

[5] Xu, J. N., Huang, N., & Song, H. (2025). Large language model-driven automated grading of multimodal experiment reports. Modern Information Technology, 9(12), 79–84.

[6] Shao, X. X., Yang, J. X., Zhao, L. B., et al. (2025). Empowering the cultivation of elite innovative talents in fundamental disciplines at local applied universities through large language models: Challenges, opportunities, and pathways. Journal of Education and Media Studies, (2), 37–42.

[7] Liao, N. Y., Tian, Y., Li, Y. S., et al. (2025). Tool learning with large language models: Methods, functions, and mechanisms. Computer Engineering, 51(12), 1–17.

[8] Huang, L. J., Li, M. Y., & Li, X. J. (2025). Reform and practice exploration of teaching models for e-commerce specialty courses under the “OBE philosophy + AI technology” approach: Taking the “Business website construction and management” course as an example. New Curriculum Research, (35), 13–15.

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Published

2026-04-15

Issue

Section

Articles

How to Cite

Zhang, Qin, Zhongqin Xie, and Huimei Wang. 2026. “Exploration and Practice of Reforming Experimental Teaching in Universities Empowered by Large Language Models: Case Study of the Integrated Virtual Simulation Training Course on Modern Enterprise Operations”. Scientific Journal Of Humanities and Social Sciences 8 (5): 135-42. https://doi.org/10.54691/4x8kp856.