Research on Experimental Teaching of Programming Courses Assisted by Large Language Models

DOI: https://doi.org/10.62517/jnse.202617307

Author(s)

Jianhua Zhao1,*, Ning Liu2

Affiliation(s)

1School of Mathematics and Computer Application, Shangluo University, Shangluo, China 2Faculty of Economics and Management, Shangluo University, Shangluo, China *Corresponding Author

Abstract

With the breakthrough development of generative artificial intelligence technology, the field of higher education is accelerating into a new stage of deep integration between large language models and traditional teaching. This paper analyzes the current status and existing problems of practical teaching in programming courses, and explores the methods and pathways for integrating large language models into the practical teaching system of programming courses. By leveraging large language models to construct professional knowledge graphs, assist personalized learning, build multimodal fusion interactive experimental scenarios, support comprehensive experimental project development, and reconstruct learning outcome evaluation, we aim to cultivate computer professionals who meet the requirements of the large-model era. Furthermore, the paper identifies issues that require attention in the educational application of large language models and proposes strategic recommendations.

Keywords

Large Language Model; Personalized Recommendation; Programming; Knowledge Graph; Industry-Education Integration; Experimental Teaching

References

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