Öğrencilerin Programlama Derslerinde Üretken Yapay Zekâ Araçlarını Kabulü: Genişletilmiş Teknoloji Kabul Modeli ile Türkiye'den Bulgular

Öz

Bu çalışma, üretken yapay zekâ (YZ) araçlarının, özellikle bilişim alanlarında öğrenim gören üniversite öğrencileri tarafından programlama derslerinde benimsenme niyetini etkileyen faktörleri incelemektedir. Teknoloji Kabul Modeli (TKM), Öz Yeterlik (ÖY), Algılanan Güven (AG), Algılanan Risk (AR) ve Bağımlılık Endişesi (BE) ile genişletilmiştir. Veriler, Türkiye’deki 305 lisans öğrencisinden çevrim içi anket yoluyla toplanmış ve Yapısal Eşitlik Modellemesi ile analiz edilmiştir. Bulgular, Algılanan Fayda (AF) ve Algılanan Kullanım Kolaylığı’nın (AKK), kullanma niyetini anlamlı biçimde yordadığını göstermektedir. AG’nin hem AF hem de AKK üzerinde pozitif etkisi bulunurken, ÖY’nin de AKK üzerinde anlamlı etkisi gözlenmiştir. Ayrıca, ÖY ile BE arasında pozitif bir ilişki tespit edilmiştir (β = 0.506). Bu sonuç, teknolojik yetkinliği yüksek öğrencilerin, potansiyel bağımlılık risklerine daha duyarlı olabileceğini göstermektedir. Çalışma, YZ araçlarının eğitimde kabulüne dair literatüre özgün katkılar sunmaktadır.

Anahtar Kelimeler

• Üretken yapay zekâ
• Programlama eğitimi
• Öz yeterlik ve bağımlılık dinamikleri

Student Acceptance of Generative AI Tools in Programming Courses: Findings from Türkiye Through an Extended Technology Acceptance Model

Öz

This study examines the cognitive and affective factors influencing university students’ intention to adopt generative artificial intelligence (AI) tools in programming courses, particularly among those studying in computing-related fields. The Technology Acceptance Model (TAM) is extended with psychological constructs including Self-Efficacy (SE), Perceived Trust (PT), Perceived Risk (PR), and Dependence Worry (DW). Data were collected from 305 undergraduate students in Türkiye via an online survey and analyzed using Structural Equation Modeling (SEM). Findings indicate that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) significantly predict students’ behavioral intention to use generative AI tools. PT positively affects both PU and PEOU, while SE significantly influences PEOU. Notably, SE also has a positive and significant relationship with DW (β = 0.506), suggesting that students with higher technological competence may be more aware of potential overdependence risks. This study contributes to the literature by highlighting the psychological dynamics underlying AI tool acceptance in educational contexts.

Anahtar Kelimeler

• Generative artificial intelligence
• Programming education
• Self-efficacy and dependence dynamics

Kaynakça

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