Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis

Beyzanur Kuşcu; Şeyma Çağlar Özhan

doi:10.24106/kefdergi.1938534

EN TR

Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis

Öz

Artificial intelligence (AI) technologies are becoming increasingly widespread across many fields, particularly in education, and have the potential to reshape teaching processes. Teachers' perceptions of AI, AI-supported learning motivation, and AI usage competence may play an important role in the effective integration of AI into educational practices. Nevertheless, empirical studies examining the relationships among teachers' AI-related perceptions, AI-supported learning motivation, and AI usage competence within a comprehensive structural model remain relatively limited. Accordingly, this study aimed to investigate, within the framework of a predictive correlational model, the effects of teachers' perceptions of AI for learning, teaching, and ethics on AI-supported learning motivation, as well as the role of this motivation in shaping AI usage competence. The study sample consisted of 342 teachers. Data were collected online using the Teachers' Perceptions of AI Use in Education Scale and the Generative AI Usage and Competence (GAIUC) Scale, and were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that teachers' perceptions of AI for learning and AI for teaching significantly predicted AI-supported learning motivation, whereas perception of AI ethics was not a significant predictor. In addition, AI-supported learning motivation was found to significantly predict AI usage competence. Mediation analysis further revealed that AI-supported learning motivation fully mediated the relationship between perception of AI for learning and AI usage competence, while partially mediating the relationship between perception of AI for teaching and AI usage competence. The findings suggest that teachers' AI-related perceptions and AI-supported learning motivation may be considered together to better explain AI usage competence. Accordingly, developing professional development programs and educational practices that strengthen teachers' AI-related perceptions and motivation may contribute to enhancing their AI usage competence.

Anahtar Kelimeler

Artificial intelligence, Teacher perception, Learning motivation, Usage competency, Predictive-relational model

Öğretmenlerin Yapay Zekâya İlişkin Algıları ve Yapay Zekâ Destekli Öğrenme Motivasyonunun Yapay Zekâ Kullanım Yeterliğinin Yordayıcıları Olarak İncelenmesi: Bir PLS-SEM Analizi

Öz

Yapay zeka (YZ) teknolojileri, birçok alanda, özellikle de eğitimde giderek yaygınlaşmakta ve öğretim süreçlerinin yeniden şekillendirebilmektedir. Öğretmenlerin yapay zeka algıları, yapay zeka destekli öğrenme motivasyonları ve kullanım yeterlilikleri düzeyleri öğretim sürecinin şekillendirilmesinde önemli roller üstlenebilmektedir. Bununla birlikte, öğretmenlerin yapay zekâya ilişkin algıları, yapay zekâ destekli öğrenme motivasyonu ve yapay zekâ kullanım yeterliği arasındaki ilişkileri bütüncül bir yapısal model çerçevesinde inceleyen ampirik araştırmaların görece sınırlı olduğu görülmektedir. Bu kapsamda bu çalışma, yordayıcı ilişkisel model çerçevesinde, öğretmenlerin yapay zekaya ilişkin öğrenme, öğretim ve etik algılarının, yapay zeka destekli öğrenmeye yönelik motivasyonları üzerindeki etkisini ve bu motivasyonun yapay zeka kullanım yeterliliğini şekillendirmedeki rolünü araştırmayı amaçlamaktadır. Araştırma grubunu 342 öğretmen oluşturmaktadır. Veriler, “Öğretmenlerin Eğitimde Yapay Zekâ Kullanımına İlişkin Algı” ölçeği ile “Üretken Yapay Zekâ Kullanım ve Yeterlik (ÜYZKY)” ölçekleri aracılığıyla dijital ortamda toplanmış ve Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi (PLS-SEM) ile analiz edilmiştir. Araştırma bulguları, öğretmenlerin yapay zekâya ilişkin öğrenme ve öğretme algılarının yapay zekâ destekli öğrenme motivasyonunu anlamlı biçimde yordadığını, etik algının ise anlamlı bir yordayıcı olmadığını göstermiştir. Ayrıca yapay zekâ destekli öğrenme motivasyonunun yapay zekâ kullanım yeterliğini anlamlı biçimde yordadığı belirlenmiştir. Aracılık analizi sonuçları, yapay zekâ destekli öğrenme motivasyonunun öğrenme algısı ile yapay zekâ kullanım yeterliği arasındaki ilişkide tam, öğretme algısı ile yapay zekâ kullanım yeterliği arasındaki ilişkide ise kısmi aracılık rolü üstlendiğini ortaya koymuştur. Bulgular, öğretmenlerin yapay zekâ kullanım yeterliklerinin açıklanmasında yapay zekâya ilişkin algılar ile yapay zekâ destekli öğrenme motivasyonunun birlikte ele alınmasının yararlı olabileceğini göstermektedir. Bu doğrultuda, öğretmenlerin YZ’ ye yönelik algı ve motivasyonlarını güçlendirmeye yönelik eğitim ve uygulamaların geliştirilmesi önerilmektedir.

Anahtar Kelimeler

Yapay zeka, Öğretmen algısı, Öğrenme motivasyonu, Kullanım yeterliği, Yordayıcı-ilişkisel model

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Öğretmen Eğitimi ve Eğitimcilerin Mesleki Gelişimi

Bölüm

Araştırma Makalesi

Yazarlar

Beyzanur Kuşcu ^*
0009-0003-6032-1340
Türkiye

Şeyma Çağlar Özhan
0000-0001-5523-0581
Türkiye

Yayımlanma Tarihi

29 Ekim 2026

Gönderilme Tarihi

27 Nisan 2026

Kabul Tarihi

15 Ekim 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 34 Sayı: 4

DOI

https://doi.org/10.24106/kefdergi.1938534

IZ

https://izlik.org/JA56GL42BY

APA

Kuşcu, B., & Çağlar Özhan, Ş. (2026). Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis. Kastamonu Education Journal, 34(4), 876-897. https://doi.org/10.24106/kefdergi.1938534

AMA

1.Kuşcu B, Çağlar Özhan Ş. Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis. Kastamonu Eğitim Dergisi. 2026;34(4):876-897. doi:10.24106/kefdergi.1938534

Chicago

Kuşcu, Beyzanur, ve Şeyma Çağlar Özhan. 2026. “Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis”. Kastamonu Education Journal 34 (4): 876-97. https://doi.org/10.24106/kefdergi.1938534.

EndNote

Kuşcu B, Çağlar Özhan Ş (01 Ekim 2026) Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis. Kastamonu Education Journal 34 4 876–897.

IEEE

[1]B. Kuşcu ve Ş. Çağlar Özhan, “Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis”, Kastamonu Eğitim Dergisi, c. 34, sy 4, ss. 876–897, Eki. 2026, doi: 10.24106/kefdergi.1938534.

ISNAD

Kuşcu, Beyzanur - Çağlar Özhan, Şeyma. “Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis”. Kastamonu Education Journal 34/4 (01 Ekim 2026): 876-897. https://doi.org/10.24106/kefdergi.1938534.

JAMA

1.Kuşcu B, Çağlar Özhan Ş. Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis. Kastamonu Eğitim Dergisi. 2026;34:876–897.

MLA

Kuşcu, Beyzanur, ve Şeyma Çağlar Özhan. “Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis”. Kastamonu Education Journal, c. 34, sy 4, Ekim 2026, ss. 876-97, doi:10.24106/kefdergi.1938534.

Vancouver

1.Beyzanur Kuşcu, Şeyma Çağlar Özhan. Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis. Kastamonu Eğitim Dergisi. 01 Ekim 2026;34(4):876-97. doi:10.24106/kefdergi.1938534

Kastamonu Eğitim Dergisi, Creative Commons Atıf 4.0 Uluslararası Lisansı (CC BY) ile lisanslanmıştır.

Teachers’ AI-Related Perceptions and AI-Supported Learning Motivation as Predictors of AI Usage Competence: A PLS-SEM Analysis

Öz

Anahtar Kelimeler

Öğretmenlerin Yapay Zekâya İlişkin Algıları ve Yapay Zekâ Destekli Öğrenme Motivasyonunun Yapay Zekâ Kullanım Yeterliğinin Yordayıcıları Olarak İncelenmesi: Bir PLS-SEM Analizi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

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