Enhancing Computational Thinking and Problemsolving in Programming Education Through Generative AI: A Scoped Review
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This study assesses how generative artificial intelligence tools enhance computational thinking and problemsolving skills in the context of programming education. Generative AI (GenAI) has ushered in a new era in programming education, offering immediate, personalised support through tools like ChatGPT and GitHub Copilot. Although generative AI tools have shown promise in enhancing immediate problem-solving abilities, there is a lack of research on their long-term effects on students' computational thinking and professional programming skills development. This study conducted a scoped evaluation of previously published papers using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard, and the data was analysed using a thematic approach. The findings from the study indicate that integrating GenAI can potentially enhance computational thinking and problem-solving for programming students. On the flipside, our study highlighted some significant ethical challenges associated with using GenAI in academia, particularly regarding issues of originality in student work. Contrary to expectations on how GenAI tools enhance learners' decomposition, abstraction, and algorithm design skills, most of the findings concentrated on students' completion of tasks. From a practical perspective, it is evident that GenAI has changed the learning landscape therefore, there is a need from a policy perspective to start thinking about the transformational roles of educators. Future studies should be carried out over a long period and should start by assessing students' levels of problem-solving at a particular age before the immersive use of GenAI and then check the results after the use of these tools.
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AI integration into education has significantly transformed teaching, learning, and assessment practices.
“Integrating artificial intelligence (AI) into education has significantly transformed teaching, learning, and assess -ment practices [ 23, 24 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [24]
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- Authors
- Matobobo, C., Ncube, P. D. N., Ngesimani, N. L., Dzvapatsva, G. P., & Chinhamo, E.
- Affiliations
- Walter Sisulu University (ZA)
- University of Suffolk (GB)
- University of Greenwich (GB)
- ORCID
- Venue
- 2025 IEEE Global Engineering Education Conference (EDUCON)
- Publisher
- IEEE
- Published
- 2025-04-22
- Pages
- 1-9
- OpenAlex
- W4410986920
- Cited by
- 17
- References
- 41
- Citations by year
- 2026: 16 · 2025: 1
- FWCI
- 24.79
- Type
- conference paper
- Language
- en
- Open access
- green
- orcid.org
- Locations
- IEEE Global Engineering Education Conference (EDUCON) (publishedVersion)
- Greenwich Academic Literature Archive (University of Greenwich) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Problem and Project Based Learning
- Keywords
- generative AI, programming education, computational thinking, problem solving, ChatGPT, GitHub Copilot, academic integrity, AI ethics in education, decomposition, abstraction, algorithm design
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