Construction of a Large Language Model-Driven Online Programming Experiment System and Research on Active Learning Paradigm Transformation

Authors

  • Shengying Yang Zhejiang University of Science and Technology, Hangzhou 310023, China Author
  • Chen Lu Zhejiang University of Science and Technology, Hangzhou 310023, China Author
  • Fangfang Qiang Zhejiang University of Science and Technology, Hangzhou 310023, China Author

DOI:

https://doi.org/10.5281/zenodo.15582458

Keywords:

Large Language Models, Programming Experiment Platform, Program Design, Artificial Intelligence

Abstract

Traditional online programming teaching platforms have significant deficiencies in supporting the development of students' abilities. The core issue is concentrated on the unidirectional nature of the evaluation system: it cannot effectively assess engineering elements such as code standardization, style uniformity, and runtime efficiency, nor can it provide students with in-depth suggestions for improvement. To break through this limitation, this paper constructs a new generation of online programming experiment platforms based on large language model technology. The platform can analyze students' code logic in real-time, generate targeted error correction suggestions, explanations of knowledge points, and optimization plans, and supports language interaction to help students quickly understand programming concepts. Experiments show that the platform significantly improves students' programming practice abilities, confirming its value in programming education. It provides an expandable technical solution for the innovation of programming education models and is of great significance in promoting the transformation of programming education from passive infusion to active exploration.

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References

Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in social and administrative pharmacy, 19(8), 1236-1242.

Savelka, J., Agarwal, A., Bogart, C., Song, Y., & Sakr, M. (2023, June). Can generative pre-trained transformers (gpt) pass assessments in higher education programming courses?. In Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 (pp. 117-123).

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and individual differences, 103, 102274.

Yu, J. H., Chang, X. Z., Liu, W., & Huan, Z. (2023). An online integrated programming platform to acquire students' behavior data for immediate feedback teaching. Computer Applications in Engineering Education, 31(3), 520-536.

Yan, Y. M., Chen, C. Q., Hu, Y. B., & Ye, X. D. (2025). LLM-based collaborative programming: impact on students’ computational thinking and self-efficacy. Humanities and Social Sciences Communications, 12(1), 1-12.

Roy, D., Zhang, X., Bhave, R., Bansal, C., Las-Casas, P., Fonseca, R., & Rajmohan, S. (2024, July). Exploring llm-based agents for root cause analysis. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering (pp. 208-219)..

Strmečki, D., Magdalenić, I., & Radosević, D. (2018). A systematic literature review on the application of ontologies in automatic programming. International journal of software engineering and knowledge engineering, 28(05), 559-591.

Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied sciences, 13(9), 5783.

Wen, W., & Wen, H. (2024). Bridging Generative AI Technology and Teacher Education: Understanding Preservice Teachers' Processes of Unit Design with ChatGPT. Contemporary Issues in Technology and Teacher Education (CITE Journal), 24(4), n4.

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Published

2025-06-03

Data Availability Statement

Acknowledgements

This work was supported by Zhejiang Higher Education Society Project "Artificial Intelligence Empowers Education and Teaching Application Research" Special Project (No. KT2024464), Zhejiang University of Science and Technology Teaching Research and Reform Project (No. 2023-jg16), and Zhejiang University of Science and Technology Graduate Course Construction Project (No. 2021yjskj04).

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Section

Articles

How to Cite

Shengying Yang, Chen Lu, & Fangfang Qiang. (2025). Construction of a Large Language Model-Driven Online Programming Experiment System and Research on Active Learning Paradigm Transformation. Global Academic Frontiers, 3(2), 99-105. https://doi.org/10.5281/zenodo.15582458

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