Research article | Open Access
Journal of Technology Applications in Education 2026, Vol 7(1) 1-31

Öğretmenlerin Yapay Zekâ Yeterliklerine İlişkin Öz-Yeterlik Ölçeği: Geçerlik ve Güvenirlik Çalışması

Article Type
Research article
Publication Date
October 30, 2026
Pages
1-31

Abstract

Bu araştırmanın amacı, öğretmenlerin yapay zekâ (YZ) teknolojilerini eğitim süreçlerinde kullanmaya yönelik öz yeterliklerini belirlemek amacıyla geçerli ve güvenilir bir ölçme aracı geliştirmektir. Bu doğrultuda UNESCO Öğretmenler için Yapay Zekâ Yeterlik Çerçevesi (YZYÇ) incelenmiş ve öğretmenlerin YZ öz yeterliği, çerçevenin aşamalı yeterlik anlayışı doğrultusunda Edinme, Derinleştirme ve Yaratma olmak üzere üç temel düzeyde kavramsallaştırılmıştır. Bu kuramsal yapı temelinde hazırlanan 45 maddelik ilk form, birinci çalışma grubundan elde edilen verilerle açımlayıcı faktör analizine tabi tutulmuştur (N = 295). Paralel analiz, özdeğer yamaç grafiği ve kuramsal yapı birlikte değerlendirilerek üç faktörlü yapı benimsenmiştir. Aşamalı madde eleme süreci sonunda 27 maddelik form elde edilmiş ve bu yapı toplam varyansın %61.57’sini açıklamıştır. İkinci çalışma grubundan elde edilen verilerle doğrulayıcı faktör analizi yapılmıştır (N = 195). Model revizyonları sonucunda 22 maddelik nihai form elde edilmiş ve üç faktörlü model kabul edilebilir uyum değerleri göstermiştir (χ²/sd = 2.54, CFI = .908, TLI = .897, IFI = .909, RMSEA = .089). Nihai ölçek Edinme (5 madde), Derinleştirme (9 madde) ve Yaratma (8 madde) olmak üzere üç boyuttan oluşmaktadır. Standardize faktör yükleri .525 ile .899 arasında değişmiştir. Alt boyutlara ilişkin güvenirlik katsayıları Edinme için α = .762, ω = .753; Derinleştirme için α = .950, ω = .951; Yaratma için α = .941, ω = .941 olarak hesaplanmıştır. Madde-toplam korelasyonları, üst-alt grup karşılaştırmaları, bileşik güvenirlik, yakınsak geçerlik ve HTMT bulguları ölçeğin psikometrik özelliklerini desteklemiştir. Elde edilen bulgular, geliştirilen 22 maddelik ölçeğin öğretmenlerin YZ teknolojilerini eğitim süreçlerinde kullanmaya yönelik öz yeterliklerini aşamalı yeterlik düzeyleri bağlamında değerlendirmede kullanılabilecek geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir.

Keywords: Yapay Zekâ Öğretmen Öz Yeterliği Ölçek Geliştirme UNESCO YZ Yeterlik Çerçevesi
Cite this article
Şirin, R. & Polat, M. (2026). Öğretmenlerin Yapay Zekâ Yeterliklerine İlişkin Öz-Yeterlik Ölçeği: Geçerlik ve Güvenirlik Çalışması. Journal of Technology Applications in Education, 7(1), 1-31. https://doi.org/10.29329/jtae.2026.1432.7

  • Aksekili, E., & Kan, A. (2024). Öğretmenlerin eğitimde yapay zekâ kullanımına yönelik tutum ölçeği geliştirme: Geçerlik ve güvenirlik çalışması. 21. Yüzyılda Eğitim ve Toplum, 13(39), 525–542. https://doi.org/10.51460/baebd.1541524 [Google Scholar] [Crossref] 
  • Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, Article 100132. https://doi.org/10.1016/j.caeai.2023.100132 [Google Scholar] [Crossref] 
  • Alwaqdani, M. (2025). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30(3), 2737–2755. https://doi.org/10.1007/s10639-024-12903-9 [Google Scholar] [Crossref] 
  • Arık, S., Özdere, M., & Çetin, M. (2026). Öğretmen adayları için yapay zeka okuryazarlığı ölçeği: Geliştirme, geçerlik ve güvenirlik çalışması. Journal of Computer and Education Research, 14, Article e2614029. https://doi.org/10.18009/jcer.1792826 [Google Scholar] [Crossref] 
  • Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, Article 100179. https://doi.org/10.1016/j.caeo.2024.100179 [Google Scholar] [Crossref] 
  • Ayre, C., & Scally, A. J. (2014). Critical values for Lawshe’s content validity ratio: Revisiting the original methods of calculation. Measurement and Evaluation in Counseling and Development, 47(1), 79–86. https://doi.org/10.1177/0748175613513808 [Google Scholar] [Crossref] 
  • Bai, J. (2025). Overview and summary of AI competency framework for teachers. Global Medical Education, 1(1). https://doi.org/10.1515/gme-2024-0029 [Google Scholar] [Crossref] 
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. [Google Scholar]
  • Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Information Age Publishing. [Google Scholar]
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press. [Google Scholar]
  • Büyük, U., & Çetingüney, H. (2025). Eğitimde yapay zekâ kullanımına yönelik öğretmenlerin öz yeterlik inancı ölçeği: Geçerlik ve güvenirlik çalışması. Batı Anadolu Eğitim Bilimleri Dergisi, 16(1), 1422–1445. https://doi.org/10.51460/baebd.1602705 [Google Scholar] [Crossref] 
  • Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468 [Google Scholar] [Crossref] 
  • Chee, H., Ahn, S., & Lee, J. (2024). A competency framework for AI literacy: Variations by different learner groups and an implied learning pathway. British Journal of Educational Technology, 55(6), 2574–2596. https://doi.org/10.1111/bjet.13556 [Google Scholar] [Crossref] 
  • Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z [Google Scholar] [Crossref] 
  • Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32, 725–755. https://doi.org/10.1007/s40593-021-00243-5 [Google Scholar] [Crossref] 
  • Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1), Article 7. https://doi.org/10.7275/jyj1-4868 [Google Scholar] [Crossref] 
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555 [Google Scholar] [Crossref] 
  • Çakır, D., & Güner, P. (2026). The current landscape of teachers’ artificial intelligence acceptance: Relationships with TPACK and technostress. International Journal of Didactical Studies, 7(3), Article e43911. https://doi.org/10.33902/ijods.202643911 [Google Scholar] [Crossref] 
  • Çelik Demirci, S., Besaltı, M., & Kul, Ü. (2026). Psychometric validation of the Turkish Brief General AI Self-Efficacy Scale: Examining its role in AI anxiety, acceptance, and demographic predictors. Frontiers in Psychology, 17, Article 1714147. https://doi.org/10.3389/fpsyg.2026.1714147 [Google Scholar] [Crossref] 
  • Çetingüney, H., & Büyük, U. (2025). Eğitimde yapay zekâ kullanımına yönelik öğretmenlerin algısı ölçeği: Geçerlik ve güvenirlik çalışması. İnönü Üniversitesi Eğitim Fakültesi Dergisi, 26(3), 2163–2210. https://doi.org/10.17679/inuefd.1602786 [Google Scholar] [Crossref] 
  • DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Sage. [Google Scholar]
  • Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, Article 100178. https://doi.org/10.1016/j.caeo.2024.100178 [Google Scholar] [Crossref] 
  • Dogan, U., Ozbay, O., Adiguzel, O., Yildiz Akan, E., & Cakir, S. (2025). Öğretmenlerin yapay zekâyı kabul ölçeğinin Türkçeye uyarlanması. Sustainability in Educational Technologies (ICITS 2025), 230–240. [Google Scholar]
  • Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046 [Google Scholar] [Crossref] 
  • Ergun, M., & Arslan, M. A. (2026). Exploring preservice science teachers’ AI-TPACK competencies and perceptions of artificial intelligence in education. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 41(2), 404–425. https://doi.org/10.16986/hunefd.1770828 [Google Scholar] [Crossref] 
  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272 [Google Scholar] [Crossref] 
  • Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency framework. Educational Technology Research and Development, 68, 2449–2472. https://doi.org/10.1007/s11423-020-09767-4 [Google Scholar] [Crossref] 
  • Ferikoğlu, D., & Akgün, E. (2022). An investigation of teachers’ artificial intelligence awareness: A scale development study. Malaysian Online Journal of Educational Technology, 10(3), 215–231. https://doi.org/10.52380/mojet.2022.10.3.407 [Google Scholar] [Crossref] 
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 [Google Scholar] [Crossref] 
  • Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, Article 102381. https://doi.org/10.1016/j.ijer.2024.102381 [Google Scholar] [Crossref] 
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning. [Google Scholar]
  • Hava, K., & Babayiğit, Ö. (2025). Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency. Education and Information Technologies, 30(3), 3491–3508. https://doi.org/10.1007/s10639-024-12939-x [Google Scholar] [Crossref] 
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref] 
  • Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447 [Google Scholar] [Crossref] 
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 [Google Scholar] [Crossref] 
  • Işık, S., Çakır, R., & Korkmaz, Ö. (2024). Teachers’ perception scale towards the use of artificial intelligence tools in education. Participatory Educational Research, 11 (Prof. Dr. H. Ferhan Odabaşı Gift Issue), 80–94. https://doi.org/10.17275/per.24.95.11.6 [Google Scholar] [Crossref] 
  • Kim, K., & Kwon, K. (2023). Exploring the AI competencies of elementary school teachers in South Korea. Computers and Education: Artificial Intelligence, 4, Article 100137. https://doi.org/10.1016/j.caeai.2023.100137 [Google Scholar] [Crossref] 
  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press. [Google Scholar]
  • 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 [Google Scholar] [Crossref] 
  • Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727 [Google Scholar] [Crossref] 
  • Lorenz, U., & Romeike, R. (2023). What is AI-PACK?–Outline of AI competencies for teaching with DPACK. In International conference on informatics in schools: Situation, evolution, and perspectives (pp. 13-25). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44900-0_2 [Google Scholar] [Crossref] 
  • Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861 [Google Scholar] [Crossref] 
  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130 [Google Scholar] [Crossref] 
  • MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111(3), 490–504. https://doi.org/10.1037/0033-2909.111.3.490 [Google Scholar] [Crossref] 
  • McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates. [Google Scholar]
  • Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3), Article e12663. https://doi.org/10.1111/ejed.12663 [Google Scholar] [Crossref] 
  • Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232 [Google Scholar] [Crossref] 
  • Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041 [Google Scholar] [Crossref] 
  • Oran, B. B. (2023). Correlation between artificial intelligence in education and teacher self-efficacy beliefs: A review. RumeliDE Dil ve Edebiyat Araştırmaları Dergisi, 34, 1354–1365. [Google Scholar]
  • Özdemir, E., & Benli Özdemir, E. (2025). Fen bilimleri öğretmenlerine yönelik yapay zekâ farkındalık ölçeğinin geliştirilmesi: Geçerlik ve güvenirlik çalışması. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, 7(Özel sayı), 117–141. https://doi.org/10.51119/ereegf.2025.140 [Google Scholar] [Crossref] 
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 [Google Scholar] [Crossref] 
  • Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: An initial investigation. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–7. https://doi.org/10.1145/3491101.3519866 [Google Scholar] [Crossref] 
  • Ramazanoglu, M., & Akın, T. (2025). AI readiness scale for teachers: Development and validation. Education and Information Technologies, 30(6), 6869–6897. https://doi.org/10.1007/s10639-024-13087-y [Google Scholar] [Crossref] 
  • Satmaz, İ. (2026). Artificial intelligence mentorship perception scale for preservice teachers: Development and preliminary psychometric evidence. Journal of Technology Applications in Education, 7, e9952. https://doi.org/10.29329/jtae.2026.1432.5 [Google Scholar] [Crossref] 
  • Sever, İ. (2026). Sosyal bilgiler öğretmen adaylarının üretken yapay zeka kabul düzeylerinin dijital okuryazarlık açısından incelenmesi. Academia Journal of Educational Research, 11(1), 1–13. [Google Scholar]
  • Shi, L. (2025). Assessing teachers’ generative artificial intelligence competencies: Instrument development and validation. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13684-5 [Google Scholar] [Crossref] 
  • Tashtoush, M. A., Wardat, Y., AlAli, R., & Jarrah, A. M. (2024). Artificial intelligence in education: Mathematics teachers’ perspectives. Iraqi Journal for Computer Science and Mathematics, 5(1), 60–77. [Google Scholar]
  • Tekin, Ö. G. (2024). Factors affecting teachers’ acceptance of artificial intelligence technologies: Analyzing teacher perspectives with structural equation modeling. Instructional Technology and Lifelong Learning, 5(2), 399–420. https://doi.org/10.52911/itall.1532218 [Google Scholar] [Crossref] 
  • Tuan, L. K., Huong, P. T. T., Ha, D. T. T., Lieu, D. T. T., & Hanh, B. T. H. (2025). Development of artificial intelligence competences for teachers in the context of Vietnam. Multidisciplinary Science Journal, 7, Article e2025605. https://doi.org/10.31893/multiscience.2025605 [Google Scholar] [Crossref] 
  • UNESCO. (2024). AI competency framework for teachers. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/ZJTE2084 [Google Scholar] [Crossref] 
  • Uyan, U., & Gültekin, S. U. (2024). Yapay zeka öz-yeterlilik ölçeğinin Türkçe’ye uyarlanması: Geçerlik ve güvenirlik çalışması. Journal of Research in Business, 9(1), 135–148. https://doi.org/10.54452/jrb.1415212 [Google Scholar] [Crossref] 
  • Uygun, D. (2024). Teachers’ perspectives on artificial intelligence in education. Advances in Mobile Learning Educational Research, 4(1), 931–939. https://doi.org/10.25082/AMLER.2024.01.005 [Google Scholar] [Crossref] 
  • Wang, Y. Y., & Chuang, Y. W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29, 4785–4808. https://doi.org/10.1007/s10639-023-12015-w [Google Scholar] [Crossref] 
  • Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9, Article 59. https://doi.org/10.1186/s40594-022-00377-5 [Google Scholar] [Crossref] 
  • Yang, Y. F., Tseng, C. C., & Lai, S. C. (2024). Enhancing teachers’ self-efficacy beliefs in AI-based technology integration into English speaking teaching through a professional development program. Teaching and Teacher Education, 144, Article 104582. https://doi.org/10.1016/j.tate.2024.104582 [Google Scholar] [Crossref] 
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0 [Google Scholar] [Crossref] 
  • Zhou, X., Li, Y. L., Chai, C. S., & Chiu, T. K. F. (2025). Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 education from a systematic review. Interactive Learning Environments, 33(10), 5766–5788. https://doi.org/10.1080/10494820.2025.2487538 [Google Scholar] [Crossref]