
Our paper, “Large Language Models in Computer Science Education: A Systematic Literature Review”, has been accepted to the prestigious conference on computer science education, 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025).
We systematically analyzed 125 papers on using LLMs in computer science education. Our findings indicate that most current research focuses on undergraduate education and introductory programming courses. They also indicate that most research applies case-based studies, while the most widely used PL is Python. Although students are usually positive about using LLMs, instructors are worried about learning effectiveness because of potential over-reliance on them. Our systemic literature review also indicates that educators are gradually adopting LLMs in their courses, but most CS curricula still need to be changed to accommodate recent advances in AI.
@inproceedings{nishat2025llmeducation,
author={Raihan, Nishat and Siddiq, Mohammed Latif and Santos, Joanna C. S. and Zampieri, Marcos},
title={Large Language Models in Computer Science Education: A Systematic Literature Review},
booktitle = {Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS '25)},
numpages = {7},
location = {Pittsburgh, Pennsylvania, United States},
series = {SIGCSE TS '25},
year={2025}
}
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Paper 37
Research 37
Tool 2
Llm 10
Dataset 2
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