Large Language Models for Computer Programming Education: A Systematic Review of Empirical Study

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Abstract

Given the increasing demands in computer programming education and the rapid advancement of large language models (LLMs), this study provides a systematic review of selected empirical studies on LLMs in computer programming education, published from 2023 to March 2024. In total, 42 studies met the selection criteria and were reviewed. This study offers an overview of the state of LLMs in computer programming education research. It outlines LLMs’ applications, benefits, limitations, concerns, and implications for future research and practices, establishing connections between LLMs and their practical use in computer programming education. This review also provides valuable insights for instructional designers, instructors, and learners. Additionally, a conceptual framework is proposed to guide education practitioners in integrating LLMs into computer programming education.

Authors

• Meina Zhu, Wayne State University

• Lanyu Xu, Oakland University

• Barbara Ericson, University of Michigan