ChatGPT in computer programming education: A review of current literature and applications

Abstract

ChatGPT has gained significant attention in computer programming education due to its advanced capabilities in assisting coding processes and its growing impact on teaching and learning. Despite rapid technological progress and widespread adoption, further research is required to optimise its integration into programming education. This scoping review used the PRISMA–ScR framework to analyse 59 research articles published between 2022 and 2025. The review identified major research areas related to ChatGPT’s use in programming education, including its role as a programming assistant, automated assessment and feedback, student and educator perceptions, curriculum design and instructional strategies, learning outcomes and performance, ethical and academic integrity considerations and applications across specific programming domains. It also examined methodological approaches, participant demographics and geographical distribution across the included studies. Findings highlight benefits of integrating ChatGPT, including enhanced student engagement, increased accessibility, support for bridging knowledge gaps and assistance with code optimisation. Meanwhile, challenges include risks of overreliance, reduced critical thinking, accuracy limitations and academic integrity concerns. This review provides practical insights for educators, universities, students and researchers. It emphasises using ChatGPT as a learning assistant, implementing clear policies, tailoring artificial intelligence (AI) tools to diverse student needs and guiding future research on effective and ethical AI-driven programming education.

Implications for practice or policy:

• ChatGPT should support debugging, exploration and collaboration rather than code generation.
• Students must annotate AI outputs, reinforced by oral exams and reflective journals.
• Educators should blend AI feedback with human evaluation through scaffolded, authentic assessments.
• Institutions need clear ethical policies, equitable access and staff training.
• Researchers should use longitudinal, mixed methods studies, while developers design explainable, adaptive and integrity-focused features aligned with course progression.