Programlama Eğitiminde Büyük Dil Modellerinin Kullanım Durumunun İncelenmesi

Year 2025, Volume: 1 Issue: 2, 74 - 104, 31.12.2025

Hüseyin Sıhat , Mehmet Akif Ocak

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

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