Yazılım Mühendisliği Eğitiminde Üretken Yapay Zekânın Bibliyometrik Analiz Yöntemi ile İncelenmesi

Abstract

Bu çalışma, üretken yapay zekâ teknolojilerinin yazılım mühendisliği eğitimine etkilerini bibliyometrik analiz yoluyla incelemektedir. Scopus veri tabanında gerçekleştirilen tarama sonucunda, üretken yapay zekâ ve yazılım mühendisliği eğitimini kesiştiren 134 yayın belirlenmiş, VOSviewer yazılımı kullanılarak anahtar kelime birlikte geçme ve ülkeler arası ortak yazarlık analizi yapılmıştır. Üretken yapay zekâ teknolojilerinin eğitim süreçlerine entegrasyonuna ilişkin mevcut eğilimler, iş birliği kalıpları ve kavramsal kümelenmeleri ortaya koyarak hem literatürdeki boşlukların tespit edilmesi hem de gelecekteki araştırmalar için yol gösterici bir temel oluşturulmasına katkı sağlamak amaçlanmaktadır. Yapılan analiz sonucunda kavramsal olarak dört tema öne çıkmaktadır: (1) büyük dil modelleri ve kod üretimi, (2) müfredat ve öğretim teknolojileri, (3) yazılım süreçleri ve kalite, (4) öğrenci merkezli pedagojik yaklaşımlar. Bulunan veriler 2024 yılının üretken yapay zekâ ve yazılım mühendisliği eğitimi alanındaki akademik yayınların yoğunlaştığı bir yıl olduğunu göstermektedir. Özellikle "computer aided instruction", "software design", "education computing", "generative AI" ve "engineering education" gibi anahtar kavramların ortalama yayın yıllarının 2024'e yakın olması bu temaların literatürde oldukça güncel ve hızla gelişen araştırma konuları olduğunu ortaya koymaktadır. Bu durum üretken yapay zekânın eğitim alanına entegrasyonunun yakın dönemde ivme kazandığını ve bu alandaki akademik ilginin arttığını göstermektedir. Çalışma üretken yapay zekânın yazılım mühendisliği eğitimine etkilerini derinlemesine anlamak ve bu alanda yapılacak yeni araştırmalara yön vermek amacıyla alanyazına önemli katkılar sunmaktadır.

Keywords

• Üretken Yapay Zekâ
• Yazılım Mühendisliği Eğitimi
• Büyük Dil Modelleri
• Bibliyometrik Analiz

Examination of Generative Artificial Intelligence in Software Engineering Education Using Bibliometric Analysis Method

Abstract

This study examines the impact of generative artificial intelligence technologies on software engineering education through bibliometric analysis. Through a search of the Scopus database, 134 publications intersecting generative AI and software engineering education were identified, and keyword co-occurrence and cross-country co-authorship analysis were conducted using VOSviewer software. By revealing current trends, patterns of collaboration, and conceptual clusters regarding the integration of generative AI technologies into educational processes, the study aims to contribute to both identifying gaps in the literature and providing a guiding basis for future research. As a result of the analysis, four conceptual themes emerge: (1) big language models and code generation, (2) curriculum and instructional technologies, (3) software processes and quality, (4) student-centered pedagogical approaches.The data shows that 2024 is a year of intensified academic publications in the field of generative AI and software engineering education. In particular, the fact that the average publication years of key concepts such as "computer aided instruction", "software design", "educational computing", "generative AI" and "engineering education" are close to 2024 reveals that these themes are very current and rapidly developing research topics in the literature. This shows that the integration of generative AI into the field of education has recently gained momentum and academic interest in this field is gradually increasing. This study provides important contributions to better understand the effects of generative AI on software engineering education in depth and to guide future research in this field.

Keywords

• Generative Artificial Intelligence
• Software Engineering Education
• Large Language Models
• Bibliometric Analysis

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