Large Language Models (LLMs) in Programming Learning: The Current Research State and Agenda

Large language models (LLMs) show great potential in programming learning. However, existing studies mainly focus on technical implementations and lack a systematic analysis of the application of LLMs in programming learning from an educational perspective. This study conducts a systematic literatur...

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Bibliographic Details
Published in:IEEE transactions on learning technologies Vol. 18; pp. 942 - 961
Main Authors: Fu, Qian, Zhao, Yaning, Jia, Zixi, Zheng, Yafeng
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Adaptation models
Bibliometrics
Codes
Education
Encoding
Generative artificial intelligence
Large language models
large language models (LLMs)
Learning (artificial intelligence)
Mathematical models
programming learning
Programming profession
Python
ISSN:1939-1382, 2372-0050
Online Access:Get full text
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Summary:Large language models (LLMs) show great potential in programming learning. However, existing studies mainly focus on technical implementations and lack a systematic analysis of the application of LLMs in programming learning from an educational perspective. This study conducts a systematic literature review and bibliometric analysis based on 75 high-quality papers, using a 6-D framework (roles, technology, learners, environment, effectiveness, and challenges) to examine the current state and agenda of LLM applications. The results indicate that the application of LLMs has evolved from model validation in 2022 to teaching applications in 2023 and is expected to be deeply integrated into the education system by 2024-2025, reflecting a shift from tools to teaching agents. In programming learning, LLMs primarily take on roles in resource generation, task solving, and feedback provision. In terms of technology usage, OpenAI's series of models dominate, with Python being the main programming language environment, and research subjects focusing on beginner programmers and university students. Empirical studies show that LLMs can effectively enhance learners' cognitive outcomes and noncognitive performance, but they can also lead to overreliance on tools, academic integrity risks, and ethical challenges. Future research should establish an education theory-driven design framework for LLMs, conduct studies on generative artificial intelligence literacy and ethical norms, and provide theoretical and practical guidance for programming learning.
ISSN:1939-1382
2372-0050
DOI:10.1109/TLT.2025.3622043