Yazılım Mühendisliği Eğitiminde Üretici Yapay Zekâ Destekli Hata Üretme ve Ayıklamaya Dayalı Öğrenimin Öğrencilerin Mesleki Öz-Yeterlilikleri Üzerindeki Etkisi

Öz

Günümüzde üretici yapay zekâ araçlarının yazılım mühendisliği eğitimine entegrasyonu, bu araçların öğrencileri “bilişsel tembelliğe” ittiği endişesi ile “verimliliği artıran bir asistan” olduğu görüşü arasındaki ikilem nedeniyle hızla genişleyen bir araştırma alanı haline gelmiştir. Özellikle Büyük Dil Modellerinin ve benzeri üretici araçların kod üretimindeki başarısı, eğitimcileri, bu araçların salt çözüm üreticisi olarak değil, öğrencilerin eleştirel düşünme ve hata ayıklama becerilerini geliştirecek pedagojik bir araç olarak nasıl konumlandırılacağı sorusuna yöneltmiştir. Bu araştırma, Kırklareli Üniversitesi Yazılım Mühendisliği Bölümü’nde öğrenim gören (1. Sınıf n = 47, 2. Sınıf n = 56, 3. Sınıf n = 11 ve 4. Sınıf n = 4), lisans öğrencileri ile gerçekleştirilmiş olup, veriler ilişkisel tarama modeli esas alınarak toplanmış ve Yapısal Eşitlik Modellemesi ile analiz edilmiştir. Bulgular, yapay zekâ desteğinin mesleki öz-yeterliliği doğrudan etkilemediğini; bunun yerine öğrencilerin derinlemesine öğrenme süreçlerini tetikleyerek ve iş birliğine dayalı hata ayıklama becerilerini güçlendirerek dolaylı ve istatistiksel olarak anlamlı bir etki yarattığını ortaya koymuştur. Özellikle Önem-Performans Haritası Analizi (IPMA) ve Gerekli Koşul Analizi (NCA) sonuçları, iş birliğine dayalı hata ayıklama deneyiminin mesleki yetkinlik inancı için en stratejik öneme sahip değişken olduğunu, ancak öğrencilerin bu süreçte en düşük performansı sergilediğini göstermiştir. Çalışma sonucunda, yapay zekânın eğitim müfredatlarına entegrasyonunda odak noktasının kod üretiminden ziyade; koddaki hataların analizi, kasıtlı hata enjeksiyonu ve hata ayıklama süreçlerine kaydırılması gerektiği vurgulanmıştır.

Anahtar Kelimeler

• Yazılım Mühendisliği Eğitimi
• Üretici Yapay Zekâ
• İşbirlikçi Hata Ayıklama
• Mesleki Öz-Yeterlilik
• İnsan-Yapay Zeka İşbirliği

The Effect of Generative Artificial Intelligence-Supported Error Generation and Debugging-Based Learning in Software Engineering Education on Students’ Professional Self-Efficacy

Abstract

The integration of generative artificial intelligence tools into software engineering education has rapidly become a growing field of research due to the dilemma between the concern that these tools push students toward “cognitive laziness” and the view that they are “productivity-enhancing assistants”. The success of Large Language Models (LLMs) and similar tools in code generation in particular has prompted educators to question how these tools can be positioned not merely as solution providers but as pedagogical tools that enhance students’ critical thinking and debugging skills. This research was conducted with undergraduate students studying in the Department of Software Engineering at Kırklareli University (1st year n = 47, 2nd year n = 56, 3rd year n = 11, and 4th year n = 4). Data were collected based on a correlational survey model and analyzed using Structural Equation Modeling. The findings revealed that artificial intelligence support did not directly affect professional self-efficacy; instead, it created an indirect and statistically significant effect by triggering students’ deep learning processes and strengthening their collaborative debugging skills. Specifically, the results of the Importance-Performance Map Analysis and the Necessary Condition Analysis showed that the collaborative debugging experience was the most strategically important variable for professional competence belief, but that students performed the worst in this process. The study concluded that the focus of integrating artificial intelligence into educational curricula should shift from code production to the analysis of errors in code, intentional error injection, and debugging processes.

Keywords

• Software Engineering Education
• Generative Artificial Intelligence
• Collaborative Debugging
• Professional Self-Efficacy
• Human-Artificial Intelligence Collaboration

Kaynakça

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