Sistematik Derlemeler ve Meta Analiz
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A Systematic Review of Studies on the Use of Generative Artificial Intelligence Tools in Programming Education

Yıl 2026, Cilt: 34 Sayı: 1, 172 - 185, 31.01.2026
https://doi.org/10.24106/kefdergi.1878122

Öz

Purpose: This study aims to systematically review the existing literature on using Generative Artificial Intelligence (Gen-AI) tools in programming education and assess their impact on educational processes.
Method: In the study, the systematic review method was adopted, following the PRISMA 2020 flow diagram guidelines. As part of the literature review, the Web of Science, ACM, IEEE, Scopus, Springer Link, Google Scholar, and The Scientific and Technological Research Council of Türkiye - National Academic Network and Information Center (TÜBİTAK ULAKBİM) databases were searched. The studies on using Gen-AI tools in programming education were compiled based on the research questions.
Findings: As a result of the literature review, 27 studies that met the specified criteria were analyzed. It was found that the majority of these studies were conducted with undergraduate students and generally focused on Python as the programming language. The most commonly used AI tool was ChatGPT. It was observed that a significant number of the studies reviewed focused on students' cognitive support gains, computational thinking skills, and their effects on their academic achievement and motivation.
Highlights: The reviews revealed that there was a significant increase in the number of academic studies on the use of Gen-AI tools in programming education, especially in 2024. However, the fact that there are only three studies on this subject in Türkiye shows that there is a big gap in the local literature. In this respect, it is thought that local studies on integrating AI tools into programming education should be increased, and there is great potential in this field.

Kaynakça

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Programlama Eğitiminde Üretken Yapay Zeka Araçlarının Kullanımına Yönelik Yapılan Çalışmalara Sistematik Bir Bakış

Yıl 2026, Cilt: 34 Sayı: 1, 172 - 185, 31.01.2026
https://doi.org/10.24106/kefdergi.1878122

Öz

Amaç: Çalışma, programlama eğitiminde Üretken Yapay Zeka (ÜYZ) araçlarının kullanımına yönelik mevcut literatürü sistematik bir şekilde incelemek ve bu araçların eğitim süreçlerine etkilerini değerlendirmeyi amaçlamaktadır.
Yöntem: Çalışmada, sistematik derleme yöntemi benimsenmiş olup, araştırma PRISMA 2020 akış şeması yönergelerine uygun olarak yürütülmüştür. Literatür taraması kapsamında; Web of Science, ACM, IEEE, Scopus, Springer Link, Google Scholar ve Türkiye Bilimsel ve Teknolojik Araştırma Kurumu - Ulusal Akademik Ağ ve Bilgi Merkezi (TÜBİTAK ULAKBİM) veri tabanları taranmıştır. Bu taramalar sonucunda, programlama eğitiminde ÜYZ araçlarının kullanımını ele alan çalışmalar araştırma soruları kapsamında derlenmiştir.
Bulgular: Literatür taraması sonucunda, belirlenen kriterleri karşılayan 27 çalışma analiz edilmiştir. Bu çalışmaların büyük çoğunluğunun lisans düzeyindeki öğrencilerle yürütüldüğü ve programlama dili olarak genellikle Python diline odaklandığı tespit edilmiştir. ÜYZ aracı olarak en yaygın kullanılan araç ise ChatGPT olmuştur. İncelenen çalışmaların önemli bir kısmının, öğrencilerin bilişsel destek kazanımları, bilgi-işlemsel düşünme becerileri, akademik başarıları ve motivasyonlarına olan etkileri üzerine yoğunlaştığı gözlemlenmiştir.
Önemli Vurgular: Yapılan incelemeler, özellikle 2024 yılında ÜYZ araçlarının programlama eğitimi alanında kullanımına dair akademik çalışmaların sayısında önemli bir artış olduğunu ortaya koymaktadır. Bununla birlikte, Türkiye'de bu konuyla ilgili yalnızca üç çalışmaya rastlanması, yerel literatürde büyük bir boşluk olduğunu göstermektedir. Bu açıdan, ÜYZ araçlarının programlama eğitimine entegrasyonu konusunda yerel çalışmaların artırılması ve bu alanda büyük bir potansiyel olduğu düşünülmektedir.

Kaynakça

  • Alonso-García, S., Aznar-Díaz, I., Caceres-Reche, M. P., Trujillo-Torres, J. M., & Romero-Rodríguez, J. M. (2019). Systematic review of good teaching practices with ICT in Spanish Higher Education. Trends and Challenges for Sustainability. Sustainability, 11(24), 7150.
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  • Aydın, N., & Erdem, O. A. (2022). A research on the new generation artificial intelligence technology generative pretraining transformer 3. In 2022 3rd International Informatics and Software Engineering Conference (IISEC) (pp. 1-6). IEEE. https://doi.org/10.1109/IISEC56263.2022.9998298
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  • Roest, L., Keuning, H., & Jeuring, J. (2024). Next-step hint generation for introductory programming using large language models. In Proceedings of the 26th Australasian Computing Education Conference (pp. 144–153). Association for Computing Machinery. https://doi.org/10.1145/3636243.3636259
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  • Reference list of studies analyzed in the research Abouelenein, Y. A. M., Ghazala, A. F. A., Mahdy, E. M. M., & Khalaf, M. H. R. (2025). The R5E pattern: can artificial intelligence enhance programming skills development?. Education and Information Technologies, 30, 22177–22205. https://doi.org/10.1007/s10639-025-13616-3 Akçapınar, G., & Sidan, E. (2024). AI chatbots in programming education: guiding success or encouraging plagiarism. Discover Artificial Intelligence, 4(1), 87. https://doi.org/10.1007/s44163-024-00203-7
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  • Fenu, G., Galici, R., Marras, M., & Reforgiato, D. (2024). Exploring student interactions with AI in programming training. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct '24). (pp. 555-560). https://doi.org/10.1145/3631700.3665227
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  • Ma, B., Chen, L., & Konomi, S. (2024). Enhancing programming education with ChatGPT: A case study on student perceptions and interactions in a Python course. In A. M. Olney, I.-A. Chounta, Z. Liu, O. C. Santos, & I. I. Bittencourt (Eds.), Artificial intelligence in education (Vol. 2150, pp. 110–116). Springer. https://doi.org/10.1007/978-3-031-64315-6_9
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  • Park, A., & Kim, T. (2025). Code suggestions and explanations in programming learning: Use of ChatGPT and performance. The International Journal of Management Education, 23(2), 101119. https://doi.org/10.1016/j.ijme.2024.101119
  • Qureshi, B. (2023). ChatGPT in computer science curriculum assessment: An analysis of its successes and shortcomings. In Proceedings of the 2023 9th International Conference on e-Society, e-Learning and e-Technologies (pp. 7–13). Association for Computing Machinery. https://doi.org/10.1145/3613944.3613946
  • Rocha, A., Sousa, L., Alves, M., & Sousa, A. (2024). The underlying potential of NLP for microcontroller programming education. Computer Applications in Engineering Education, e22778. https://doi.org/10.1002/cae.22778
  • Sheese, B., Liffiton, M., Savelka, J., & Denny, P. (2024). Patterns of student help-seeking when using a large language model-powered programming assistant. In Proceedings of the 26th Australasian Computing Education Conference (pp. 49–57). Association for Computing Machinery. https://doi.org/10.1145/3636243.3636249
  • Suh, J., Lee, K., & Lee, J. (2025). Programming education with ChatGPT: outcomes for beginners and intermediate students. Education and Information Technologies, 1-26. https://doi.org/10.1007/s10639-025-13542-4
  • Sun, D., Boudouaia, A., Zhu, C., & Li, Y. (2024). Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study. International Journal of Educational Technology in Higher Education, 21(1), 14. https://doi.org/10.1186/s41239-024-00446-5
  • Yang, A. C., Lin, J. Y., Lin, C. Y., & Ogata, H. (2024). Enhancing python learning with PyTutor: Efficacy of a ChatGPT-Based intelligent tutoring system in programming education. Computers and Education: Artificial Intelligence, 7, 100309. https://doi.org/10.1016/j.caeai.2024.100309 Yang, T. C., Hsu, Y. C., & Wu, J. Y. (2025). The effectiveness of ChatGPT in assisting high school students in programming learning: evidence from a quasi-experimental research. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2025.2450659
  • Yilmaz, R., & Karaoglan-Yilmaz, F. G. (2023a). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005
  • Yilmaz, R., & Karaoglan-Yilmaz, F. G. (2023b). The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, 100147. https://doi.org/10.1016/j.caeai.2023.100147
Toplam 70 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri (Diğer)
Bölüm Sistematik Derlemeler ve Meta Analiz
Yazarlar

Emre Özgül

Gönderilme Tarihi 18 Kasım 2024
Kabul Tarihi 6 Ekim 2025
Yayımlanma Tarihi 31 Ocak 2026
Yayımlandığı Sayı Yıl 2026 Cilt: 34 Sayı: 1

Kaynak Göster

APA Özgül, E. (2026). A Systematic Review of Studies on the Use of Generative Artificial Intelligence Tools in Programming Education. Kastamonu Education Journal, 34(1), 172-185. https://doi.org/10.24106/kefdergi.1878122

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