Programlama Eğitiminde Büyük Dil Modellerinin Kullanım Durumunun İncelenmesi

Abstract

Bu çalışma, büyük dil modellerinin (LLM) programlama eğitimindeki kullanım durumunu bibliyometrik yöntemlerle analiz etmeyi amaçlamaktadır. Yapay zekâ tabanlı araçların eğitim süreçlerine entegrasyonunun hızla artması, bu alandaki akademik üretimin sistematik biçimde incelenmesini gerekli kılmaktadır. Web of Science veri tabanından 2020–2025 yılları arasında yayımlanan 605 akademik yayın, VOSviewer yazılımı aracılığıyla analiz edilmiştir. Anahtar kelime eşgörünüm analizi, yazar ve ülke iş birliği ağı, bibliyografik eşleşme ve ortak atıf analizleri kullanılarak alandaki eğilimler ortaya konmuştur. Bulgular, "ChatGPT", "ai-assisted learning" ve "intelligent tutoring systems" gibi kavramların alanyazında öne çıktığını; yayınların özellikle 2024–2025 yıllarında yoğunlaştığını ve ABD, Çin, Tayvan gibi ülkelerin bilimsel üretimde başı çektiğini göstermektedir. Türkiye'nin yayın hacminin görünür olmakla birlikte atıf etkisinin sınırlı kaldığı saptanmıştır. Yazar iş birliklerinin çoğunlukla yerel kümeler içinde yoğunlaştığı; atıf analizlerinde bazı çalışmaların referans kaynağı niteliği taşıdığı belirlenmiştir. Çalışma, programlama eğitimi ve yapay zekâ teknolojilerinin kesişimindeki akademik yönelimleri haritalayarak alana özgü yapısal bir çerçeve sunmakta; gelecekteki araştırmalar için odaklanılması gereken başlıkları işaret etmektedir.

Keywords

• Büyük Dil Modelleri
• Programlama Eğitimi
• Bibliyometrik Analiz

An Investigation into the Use of Large Language Models in Programming Education

Abstract

This study aims to analyze the use of large language models (LLMs) in programming education through bibliometric methods. As AI-powered tools are increasingly integrated into educational processes, a systematic examination of the growing academic output in this field has become essential. A total of 605 academic publications published between 2020 and 2025 were retrieved from the Web of Science database and analyzed using VOSviewer software. Keyword co-occurrence, author and country co-authorship, bibliographic coupling, and co-citation analyses were conducted to identify research trends. The findings reveal that concepts such as “ChatGPT,” “AI-assisted learning,” and “intelligent tutoring systems” have become central in the literature, with a notable publication peak in 2024–2025. The United States, China, and Taiwan lead in scientific output, while Türkiye, although visible in terms of publication volume, remains limited in citation impact. Author collaborations were found to be concentrated within regional clusters, and certain publications emerged as key reference points in the field based on citation analyses. This study provides a structural mapping of the intersection between programming education and artificial intelligence technologies, offering a comprehensive framework for understanding the field’s academic trajectory and highlighting critical areas for future research.

Keywords

• Large Language Models
• Programming Education
• Bibliometric Analysis

References

1. Abbas, N., & Atwell, E. (2025). Cognitive computing with large language models for student assessment feedback. Big Data and Cognitive Computing, 9(5), 21, Article 112. https://doi.org/10.3390/bdcc9050112
2. Abdulla, S., Ismail, S., Fawzy, Y., & Elhaj, A. (2024). Using ChatGPT in teaching computer programming and studying its impact on students’ performance. Electronic Journal of E-Learning, 22(6), 66–81. https://doi.org/10.34190/ejel.22.6.3380
3. Abdulla, S., Ismail, S., Fawzy, Y., & Elhaj, A. (2024). Using ChatGPT in teaching computer programming and studying its impact on students’ performance. Electronic Journal of E-Learning, 22(6), 66–81. https://doi.org/10.34190/ejel.22.6.3380
4. Alfirevic, N., Pranicevic, D., & Mabic, M. (2024). Custom-trained large language models as open educational resources: An exploratory research of a business management educational chatbot in Croatia and Bosnia and Herzegovina. Sustainability, 16(12), 4929. https://doi.org/10.3390/su16124929
5. Alkafaween, U., Albluwi, I., & Denny, P. (2025). Automating autograding: Large language models as test suite generators for introductory programming. Journal of Computer Assisted Learning, 41(1), Article e13100. https://doi.org/10.1111/jcal.13100
6. Almohesh, A. (2024). AI application (ChatGPT) and Saudi Arabian primary school students' autonomy in online classes: Exploring students’ and teachers’ perceptions. International Review of Research in Open and Distributed Learning, 25(3), Article 7641. https://doi.org/10.19173/irrodl.v25i3.7641
7. Alshammari, M. (2025). An investigation into ChatGPT-enhanced adaptive e-learning systems. TEM Journal — Technology Education Management Informatics, 14(1), 503–510. https://doi.org/10.18421/tem141-45
8. Araujo, S., & Cruz-Correia, R. (2024). Incorporating ChatGPT in medical informatics education: Mixed methods study on student perceptions and experiential integration proposals. JMIR Medical Education, 10, Article e51151. https://doi.org/10.2196/51151

9. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
10. Arum, R., Leon, M., Li, X., & Lopes, J. (2025). ChatGPT early adoption in higher education: Variation in student usage, instructional support, and educational equity. AERA Open, 11, Article 23328584251331956. https://doi.org/10.1177/23328584251331956
11. Arun, G., Perumal, V., Urias, F., Ler, Y., Tan, B., Vallabhajosyula, R., Tan, E., Ng, O., Ng, K., & Mogali, S. (2024). ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: A comparative pilot study. Anatomical Sciences Education, 17(7), 1396–1405. https://doi.org/10.1002/ase.2502
12. Aster, A., Ragaller, S., Raupach, T., & Marx, A. (2025). ChatGPT as a virtual patient: Written empathic expressions during medical history taking [Early access]. Medical Science Educator. https://doi.org/10.1007/s40670-025-02342-7
13. Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., Stracke, C. M., Singh, L., & Crompton, H. (2024). The manifesto for teaching and learning in a time of generative AI: A critical collective stance to better navigate the future. Open Praxis, 16(4), 487–513. https://doi.org/10.55982/openpraxis.16.4.777
14. Burke, D., & Crompton, H. (2024). Navigating the future: Reflections on AI in higher education. In H. Crompton & D. Burke (Eds.), Artificial Intelligence Applications in Higher Education: Theories, Ethics, and Case Studies for Universities (pp. 321-331). Routledge. https://doi.org/10.4324/9781003440178
15. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22, Article 22. https://doi.org/10.1186/s41239-023-00392-8
16. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070
17. Esiyok, E., Gokcearslan, S., & Kucukergin, K. G. (2025). Acceptance of educational use of AI chatbots in the context of self-directed learning with technology and ICT self-efficacy of undergraduate students. International Journal of Human–Computer Interaction, 41(1), 641–650. http://doi.org/10.1080/10447318.2024.2303557
18. Fan, G., Liu, D., Zhang, R., & Pan, L. (2025). The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches. International Journal of Stem Education, 12(1), 17, Article 16. https://doi.org/10.1186/s40594-025-00537-3
19. Hong, A., & Hong, G. (2024). The effectiveness of coding LLMs and the challenges in teaching CS1/2: A case study. Journal of Computing Sciences in Colleges, 40(1), 122-131. https://doi.org/10.5555/3715602.3715619
20. Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
21. Karaağaçlı, M. (2025). Yapay zeka uygulamalarında etik gereksinimi. Uluslararası Bilişim Sistemleri ve Uygulamaları Dergisi, 1(1), 1–18.
22. Kazemitabaar, M., Ye, R., Wang, X., Henley, A. Z., Denny, P., Craig, M., & Grossman, T. (2024, May). CodeAid: Evaluating a classroom deployment of an LLM-based programming assistant that balances student and educator needs. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1–20). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642773
23. Kiesler, N., Lohr, D., & Keuning, H. (2023, October). Exploring the potential of large language models to generate formative programming feedback. In 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE. https://doi.org/10.48550/arXiv.2309.00029
24. Koç, A., Şimşir, İ., Bağış, M., Orhan, U., & Çevik, Z. (2022). Bir literatür incelemesi aracı olarak bibliyometrik analiz (3. basım). Nobel Yayıncılık, Ankara.
25. Lazarides, M. K., Lazaridou, I. Z., & Papanas, N. (2025). Bibliometric analysis: Bridging informatics with science. The International Journal of Lower Extremity Wounds, 24(3), 515–517. https://doi.org/10.1177/15347346231153538
26. Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional De La información, 29(1). https://doi.org/10.3145/epi.2020.ene.03
27. Ng, D. T. K., Su, J., Leung, J. K. L., & Chu, S. K. W. (2024). Artificial intelligence (AI) literacy education in secondary schools: a review. Interactive Learning Environments, 32(10), 6204-6224.. https://doi.org/10.1080/10494820.2023.2255228
28. Ng, D.T.K., Leung, J.K.L., Su, J. et al. Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Education Tech Research Dev 71, 137–161 (2023). https://doi.org/10.1007/s11423-023-10203-6
29. Ocak, M. A., Çakır, Ö., & Erdoğdu, F. (2022). Eğitimde yapay zekâ uygulamaları. H. Çakır & Ç. Uluyol (Ed.), Yapay zekâ: Kuramdan uygulamaya (s. 517–538). Nobel Akademik Yayıncılık.
30. Park, Y., & Shin, Y. (2021). Tooee: A novel scratch extension for K-12 big data and artificial intelligence education using text-based visual blocks. IEEE Access, 9, 149630-149646. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9599669
31. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
32. Peláez-Sánchez, I. C., Velarde-Camaqui, D., & Glasserman-Morales, L. D. (2024). The impact of large language models on higher education: Exploring the connection between AI and Education 4.0. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1392091
33. Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783
34. Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. https://doi.org/10.1109/ACCESS.2023.3268224
35. 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
36. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
37. Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly impact: Methods and practice (pp. 285-320). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-10377-8_13
38. Viberg, O., Wong, J., Feldman Maggor, Y., Dunder, N., & Epp, C. D. (2025). Chatting with code: Exploring LLMs as learning partners in programming education. Artificial Intelligence in Education - 26th International Conference, AIED 2025, Proceedings, 453–461. https://doi.org/10.1007/978-3-031-98465-5_57
39. Yılmaz, Z., Galanti, T. M., Naresh, N., & Kanbir, S. (2025). Exploring the interactions among instructor, prospective teachers and AI in facilitating mathematics learning. School Science and Mathematics, 14. https://doi.org/10.1111/ssm.18341
40. Yilmaz, R., & Yilmaz, F. G. K. (2023). 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
41. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
42. Whu, M., Xu, L., & Ericson, B. (2025). A systematic review of research on large language models for computer programming education. arXiv preprint arXiv:2506.21818. https://doi.org/10.48550/arXiv.2410.16349