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Öğretmen Adaylarının Yapay Zekaya Yönelik Tutumları: Bir Ölçek Geliştirme Çalışması

Yıl 2025, Cilt: 8 Sayı: 4, 608 - 621, 24.12.2025

Öz

Bu çalışma, öğretmen adaylarının yapay zekâya yönelik tutumlarını ölçmek amacıyla bir ölçme aracı geliştirmeyi hedeflemektedir. Nicel araştırma yöntemiyle gerçekleştirilen araştırmada, 278 öğretmen adayı uygun örnekleme yöntemiyle seçilmiştir. Veriler, çevrimiçi ortamda toplanarak kapsam geçerliği, açımlayıcı faktör analizi (AFA), güvenirlik analizleri ve madde-toplam korelasyonları ile analiz edilmiştir. AFA sonuçlarına göre ölçek, bilişsel, duyuşsal ve davranışsal olmak üzere üç faktörlü bir yapı sergilemiş ve toplam varyansın %53.40’ını açıklamıştır. Ölçek maddelerinin faktör yükleri 0.487 ile 0.913 arasında değişirken, Cronbach alfa güvenirlik katsayısı 0.887 olarak hesaplanmıştır. Duyuşsal boyutun geçerliliği diğer boyutlara göre daha düşük bulunmuş, bu durum duygusal ifadelerin bilişsel ve davranışsal tutumlara kıyasla daha az belirgin olduğu şeklinde yorumlanmıştır. Sonuç olarak, geliştirilen ölçek, öğretmen adaylarının yapay zekâ kullanımına yönelik tutumlarını değerlendirmek için geçerli ve güvenilir bir araç olarak önerilmektedir. Gelecekteki çalışmalarda, ölçeğin farklı sosyo-demografik değişkenlerle ilişkilendirilmesi ve duyuşsal boyutun nitel verilerle desteklenmesi önerilmektedir. Bu araştırma, eğitimde yapay zekâ teknolojilerinin etkili entegrasyonu için öğretmenlerin ve öğretmen adaylarının tutumlarının anlaşılmasının önemini vurgulamaktadır.

Kaynakça

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Preservice Teachers' Attitudes toward Artificial Intelligence: A Scale Development Study

Yıl 2025, Cilt: 8 Sayı: 4, 608 - 621, 24.12.2025

Öz

The purpose of this study is to develop a scale to assess pre-service teachers' attitudes toward artificial intelligence. This quantitative study was conducted with 278 pre-service teachers enrolled in faculties of education, who were selected through convenience sampling. The data were analyzed through content validity, exploratory factor analysis (EFA), reliability evaluation, and item-total correlations. The EFA results identified a three-factor structure as cognitive, affective, and behavioral attitude that accounted for 53.40% of the variance. The Cronbach's alpha coefficient was 0.887, and the factor loadings ranged from 0.487 to 0.913. The affective dimension showed relatively lower validity compared to the other dimensions, suggesting that emotional expressions were less pronounced than cognitive and behavioral attitudes. In conclusion, the developed scale is a valid and reliable tool for assessing pre-service teachers' views on artificial intelligence. Future studies may explore its relationship with various socio-demographic variables and strengthen the affective dimension with qualitative data. This research underscores the importance of understanding pre-service teachers’ and teachers’ attitudes for the successful integration of AI technologies in education.

Kaynakça

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  • Hwang, G. J., Xie, H., Wah, B. W., and Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. [https://doi.org/10.1016/j.caeai.2020.100001](https://doi.org/10.1016/j.caeai.2020.100001)
  • Ibuot, U. P. (2020). Attitude components and their influence on communication. African Journal for the Psychological Study of Social Issues, 23(2), 154-164.
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  • Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., and Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human-Computer Interaction, 40(2), 497-514. [https://doi.org/10.1080/10447318.2022.2151730](https://doi.org/10.1080/10447318.2022.2151730)
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  • Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers' behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328. [https://doi.org/10.1016/j.caeai.2024.100328](https://doi.org/10.1016/j.caeai.2024.100328)
  • Küçükkara, M. F., Ünal, M., and Sezer, T. (2024). Okul öncesi eğitimi öğretmenlerinin yapay zekâya ilişkin görüşleri. Temel Eğitim Araştırmaları Dergisi, 4(1), 17–28. [https://doi.org/10.55008/te-ad.1431142](https://doi.org/10.55008/te-ad.1431142)
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575. [https://doi.org/10.1111/j.1744-6570.1975.tb01393.x](https://doi.org/10.1111/j.1744-6570.1975.tb01393.x)
  • Li, P., and Wang, B. (2023). Artificial intelligence in music education. International Journal of Human-Computer Interaction, 39(1), 1-10. [https://doi.org/10.1080/10447318.2023.2209984](https://doi.org/10.1080/10447318.2023.2209984)
  • Lindner, A., and Berges, M. (2020, October). Can you explain AI to me? Teachers' pre-concepts about Artificial Intelligence. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1–9). IEEE. [https://doi.org/10.1109/FIE44824.2020.9274136](https://doi.org/10.1109/FIE44824.2020.9274136)
  • Mart, M., and Kaya, G. (2024). Okul öncesi öğretmen adaylarının yapay zekâya yönelik tutumları ve yapay zekâ okur yazarlığı arasındaki ilişkinin incelenmesi. Edutech Research, 2(1), 91-109.
  • Meylani, R. (2024). Artificial intelligence in the education of teachers: A qualitative synthesis of the cutting-edge research literature. Journal of Computer and Education Research, 12(24), 600-637.
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  • Pan, M., Wang, J., and Wang, J. (2023, November). Application of artificial intelligence in education: Opportunities, challenges, and suggestions. In 2023 13th International Conference on Information Technology in Medicine and Education (ITME) (pp. 623-627). IEEE.
  • Pokrivcakova, S. (2023). Pre-service teachers' attitudes towards artificial intelligence and its integration into EFL teaching and learning. Journal of Language and Cultural Education, 11(3), 100-114. [https://doi.org/10.2478/jolace-2023-0031](https://doi.org/10.2478/jolace-2023-0031)
  • Salvucci, S., Walter, E., Conley, V., Fink, S., and Saba, M. (1997). Measurement Error Studies at the National Center for Education Statistics.
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  • Sanusi, I. T., Ayanwale, M. A., and Tolorunleke, A. E. (2024). Investigating pre-service teachers' artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 6, 100202. [https://doi.org/10.1016/j.caeai.2024.100202](https://doi.org/10.1016/j.caeai.2024.100202)
  • Savaş, S. (2021). Artificial intelligence and innovative applications in education: The case of Turkey. Journal of Information Systems and Management Research, 3(1), 14-26.
  • Southworth, J., Migliaccio, K., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., and Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. [https://doi.org/10.1016/j.caeai.2023.100127](https://doi.org/10.1016/j.caeai.2023.100127)
  • Svenningsson, J., Höst, G., Hultén, M., and Hallström, J. (2022). Students' attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct. International Journal of Technology and Design Education, 32(3), 1531-1551. [https://doi.org/10.1007/s10798-021-09657-7](https://doi.org/10.1007/s10798-021-09657-7)
  • Şahin, M. G., & Yıldırım, Y. (2024). The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study. International Journal of Assessment Tools in Education, 11(2), 303-319. [https://doi.org/10.21449/ijate.1369023](https://doi.org/10.21449/ijate.1369023)
  • Şata, M. (2020). Nicel araştırma yaklaşımları. E. Oğuz. (Ed.), Eğitimde araştırma yöntemleri (1.baskı) içinde (s. 77-90). Eğiten Kitap Yayınları.
  • Tabachnick, B. G. and Fidell, L. S. (2013). Using multivariate statistics. Boston: Pearson.
  • Telli, S. G., & Aydın, S. (2025). Üniversitelerde yapay zekânın kullanımı: Dönüşümler, getiriler ve geleceğe hazırlık. Üniversite Araştırmaları Dergisi/ Journal of University Research, 8(1), 139-148. [https://doi.org/10.32329/uad.1609305](https://doi.org/10.32329/uad.1609305)
  • Telli-Yamamoto, G., & Karaman, F. (2011). Education 2.0. On the Horizon, 19(2), 109–117. [https://doi.org/10.1108/10748121111138308](https://doi.org/10.1108/10748121111138308)
  • Terzi, R. (2020). An adaptation of artificial intelligence anxiety scale into Turkish: Reliability and validity study. International Online Journal of Education and Teaching, 7(4), 1501-1515.
  • Toro-Espinoza, M. F., Montalván-Espinoza, J. A., and Masabanda-Vaca, M. A. (2023). Aplicación de la inteligencia artificial en el aprendizaje universitario. Revista Científica Arbitrada de Investigación en Comunicación, Marketing y Empresa REICOMUNIC, 6(12), 153-172. [https://doi.org/10.46296/rc.v6i12edespoct.0168](https://doi.org/10.46296/rc.v6i12edespoct.0168)
  • Turkaya, A., & Özdemir, E. B. (2024). Yapay zekâ teknolojileri kullanımının ön lisans öğrencilerinin dijital okuryazarlık düzeylerine etkisi. Üniversite Araştırmaları Dergisi/ Journal of University Research, 7(4), 400-465. [https://doi.org/10.32329/uad.1486583](https://doi.org/10.32329/uad.1486583)
  • Üzüm, B., Elçiçek, M., and Pesen, A. (2024). Development of teachers' perception scale regarding artificial intelligence use in Education: Validity and reliability study. International Journal of Human–Computer Interaction, 1-12. [https://doi.org/10.1080/10447318.2024.2385518](https://doi.org/10.1080/10447318.2024.2385518)
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  • Vivar, J. M. F., and Peñalvo, F. J. G. (2023). Reflexiones sobre la ética, potencialidades y retos de la inteligencia artificial en el marco de la educación de calidad (ODS4). Comunicar, 74, 37-47. [https://doi.org/10.3916/C74-2023-03](https://doi.org/10.3916/C74-2023-03)
  • Vuorikari, R., Kluzer, S., and Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens-with new examples of knowledge, skills and attitudes. Publications Office of the European Union, Luxembourg. [https://doi.org/10.2760/115376](https://doi.org/10.2760/115376)
  • Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. [https://doi.org/10.1016/j.eswa.2024.124167](https://doi.org/10.1016/j.eswa.2024.124167)
  • Wilson, F. R., Pan, W., and Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe's content validity ratio. Measurement and Evaluation in Counseling and Development, 45(3), 197-210. [https://doi.org/10.1177/0748175612440286](https://doi.org/10.1177/0748175612440286)
  • Xu, W., and Ouyang, F. (2022). A systematic review of AI role in the educational system based on a proposed conceptual framework. Education and Information Technologies, 27, 4195-4223. [https://doi.org/10.1007/s10639-021-10774-y](https://doi.org/10.1007/s10639-021-10774-y)
  • Yetişensoy, O. (2024). Tomorrow's teachers and artificial intelligence: Exploring attitudes and perceptions of Turkish prospective social studies teachers. Eurasian Journal of Teacher Education, 5(1), 1-31.
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  • Zormanová, L. (2024). Attitudes of Czech teachers towards the use of artificial intelligence in schools. Horyzonty Wychowania, 23(65), 31-41.[1]
Toplam 78 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yükseköğretim Çalışmaları (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Revşan Calp 0009-0008-0252-6141

Gamze Mukba 0000-0003-2287-4115

Mehmet Şata 0000-0003-2683-4997

Gönderilme Tarihi 11 Temmuz 2025
Kabul Tarihi 30 Kasım 2025
Yayımlanma Tarihi 24 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 8 Sayı: 4

Kaynak Göster

APA Calp, R., Mukba, G., & Şata, M. (2025). Preservice Teachers’ Attitudes toward Artificial Intelligence: A Scale Development Study. Journal of University Research, 8(4), 608-621.

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