Utilizing Large Language Models to Assist in the Teaching of Fundamentals of Computer Courses

DOI: https://doi.org/10.62517/jbdc.202501316

Author(s)

Yi Qiao, JunSong Ren

Affiliation(s)

Sichuan Polytechnic University, Deyang, Sichuan, China

Abstract

The Fundamentals of Computer Science course faces the teaching contradiction between "broad coverage" and "in-depth delivery". Against the backdrop of educational digital transformation, Large Language Models (LLMs) provide a new possibility to resolve this contradiction. This paper systematically analyzes the role positioning, practical functions, and implementation paths of LLMs in the teaching of this course: from the teacher’s perspective, LLMs can assist in constructing knowledge graphs, generating question banks and automatic grading, and enriching teaching activities; from the student’s perspective, LLMs can help solidify foundational theories, assist in knowledge deduction, cultivate programming competencies, and broaden academic horizons. On this basis, the paper proposes a "Teacher-Medium-Student-LLM" four-in-one collaborative teaching model, and conducts a teaching demonstration with the "linked list module" in data structures as an example. Meanwhile, the paper also deeply discusses potential issues in the application of LLMs, such as data privacy risks, students’ over-reliance, knowledge "hallucinations", and the inability to replace teachers’ role in moral and value cultivation. The research aims to provide ideas for teaching innovation in computer-related fields, and also offer references for educators to adapt to the era of educational large models and update their teaching philosophies and methods.

Keywords

Computer Fundamentals; Undergraduate Education; Large Language Models; Artificial Intelligence

References

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