A bibliometric exposition and review on leveraging LLMs for programming education - Amos, Joanah Pwanedo; Amodu, Oluwatosin Ahmed; Raja Mahmood, Raja Azlina; Abdulqudus, Akanbi Bolakale; Zakaria, Anies Faziehan; Iyanda, Abimbola Rhoda; Ali Bukar, Umar; Mohd Hanapi, Zurina (Institute of Electrical and Electronics Engineers, 2025)
The world is experiencing an AI revolution, with large language models (LLMs) transforming various industries, including education. Academics are striving to harness the potential of LLMs while also contending with their risks. This paper presents the first bibliometric analysis focused on LLM research in programming education, identifying leading countries, authors, and institutions while analyzing key terms and popular keywords in this field. Additionally, it highlights influential studies on topics such as introductory programming, computer science, computing, programming education, and prompt engineering, discussing key insights from these works. Findings indicate that LLMs could play a significant role in programming education and may be integrated into computer science curricula. However, careful consideration is needed to ensure their benefits outweigh their risks across various use cases. This study specifically examines ChatGPT as a representative LLM, exploring its benefits and limitations as both a learning aid for students and a support tool for professionals. It also evaluates the quality of ChatGPT-generated code and its effectiveness in simplifying programming concepts for beginners. Furthermore, the ethical implications of increasing reliance on LLMs for programming tasks, including concerns about dependency, plagiarism, and potential effects on critical thinking, are addressed. By contributing to the ongoing discourse on integrating AI tools like ChatGPT in programming education, this research emphasizes the importance of responsible and ethical usage to maximize benefits for students, educators, and the broader educational community.