AI-TPACK Ölçeği'nin Türkçe Versiyonu'nun Psikometrik Özellikleri

Year 2025, Volume: 10 Issue: 2, 135 - 148, 02.01.2026

Barzan Batuk , Nuri Türk , Azmi Türkan , Oğuzhan Yıldırım

https://doi.org/10.32568/jfce.1832424

https://izlik.org/JA28GA65FY

Abstract

Yapay Zeka (YZ) eğitim dünyasında birçok paradigma değişikliğine yol açmıştır. YZ araçlarının eğitime entagrasyonunda belirleyici olan değişkenlerden biri, öğretmenlerin AI-TPACK yeterlilikleridir. Bu araştırmada AI-TPACK Ölçeği'nin Türkçe'ye uyarlanması amaçlanmıştır. Çalışma örneklemi 316 öğretmenden oluşmaktadır. Araştırmada veri toplama aracı olarak AI-TPACK Ölçeği ve AI Tutum Ölçeği kullanılmıştır. AI-TPACK Ölçeği ile ilgili yapılan güvenirlik analizleri sonucuna göre, Cronbach alfa ve McDonald's omega değerleri .88 olarak bulunmuştur. Doğrulayıcı Faktör Analizi (DFA) sonuçları ise AI-TPACK Ölçeği uyum indekslerinin iyi düzeyde olduğunu kanıtlamıştır. Ölçeğin madde faktör yük değerleri, madde ayırt edicilik indeksleri ve yakınsak geçerlilik bulguları kabul edilebilir düzeydedir. Ölçüt geçerliliği için kullanılan AI Tutum ve AI-TPACK arasında anlamlı pozitif ilişkiler olduğu görülmüştür. Dolayısıyla araştırmanın tüm sonuçları, AI-TPACK Ölçeği'nin Türk kültüründe çalışmalarda kullanılabilecek güvenilir ve geçerli bir ölçme aracı olduğunu doğrulamaktadır.

Keywords

Yapay Zeka , AI-TPACK , Yapay Zeka Turum

Psychometric Properties of the Turkish Version of the AI-TPACK Scale

https://izlik.org/JA28GA65FY

Abstract

Artificial Intelligence (AI) has led to many paradigms shifts in the world of education. One of the variables determining the integration of AI tools into education is teachers' AI-TPACK competencies. This study aimed to adapt the AI-TPACK Scale to Turkish. The study sample consisted of 316 teachers. The AI-TPACK Scale and the AI Attitude Scale were used as data collection tools in the study. According to the results of the reliability analyses conducted on the AI-TPACK Scale, Cronbach's alpha and McDonald's omega values were found to be .88. Confirmatory Factor Analysis (CFA) results proved that the fit indices of the AI-TPACK Scale were at an acceptable level. The scale's item factor loadings, item discrimination indices, and convergent validity findings were at acceptable levels. Significant positive relationships were observed between the AI Attitude and AI-TPACK used for criterion validity. Therefore, all the results of the study confirm that the AI-TPACK Scale is a reliable and valid measurement tool that can be used in studies in Turkish culture.

Keywords

Artificial Intelligenc , AI-TPACK , AI Attitude

References

• Adeleye, O. O., Eden, C. A., & Adeniyi, I. S. (2024). Innovative teaching methodologies in the era of artificial intelligence: A review of inclusive educational practices. World Journal of Advanced Engineering Technology and Sciences, 11(2), 069-079.
• Akgün, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
• Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146. https://doi.org/10.29023/alanyaakademik.833668
• Alan, B., Zengin, F., & Keçeci, G. (2024). Yapay Zekâ Tutum Ölçeği (YZTÖ): Geçerlik ve Güvenirlik Çalışması. Cumhuriyet Uluslararası Eğitim Dergisi, 13(4), 789-800.
• Batuk, B., Aktu, Y., & Türk, N. (2025a). Yapay zeka kabul ölçeği kısa formu’nun psikometrik özelliklerinin incelenmesi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Uygarlığın Dönüşümü: Yapay Zekâ), 438-451. https://doi.org/10.35379/cusosbil.1695975
• Batuk, B., Türk, N., & Yıldırım, O. (2025b). Psychometric Properties of the Turkish Version of the AI Mindset Scale. International Journal of English for Specific Purposes. 10.70870/joinesp.1823525
• Bayram. N. (2013). Yapısal eşitlik modellemesine giriş (3.baskı). Ezgi Kitapevi.
• Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., Demirel, F. (2014). Bilimsel araştırma yöntemleri (18th ed.). Pegem Akademi Publications.
• Büyüköztürk, Ş. (2018). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, 32(32), 470-483.
• Can, A. (2020). Spss ile Bilimsel Araştırma Sürecinde Nicel Veri Analizi (9th Ed.). Ankara: Pegem Akademi.
• Çelik, 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, 107468. https://doi.org/10.1016/j.chb.2022.107468
• Chiu, T. K., Falloon, G., Song, Y., Wong, V. W., Zhao, L., & Ismailov, M. (2024). A self-determination theory approach to teacher digital competence development. Computers & education, 214, 105017.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2.nd ed.). Hillside, NJ: Lawrence Erlbaum Associates.
• Çakan, M., & Akın, A. (2024). Yapay Zeka Tutum ve Değişime Hazır Olma: İki Ölçek Uyarlama Çalışması. Econder Uluslararası Akademik Dergi, 8(2), 137-167. https://doi.org/10.35342/econder.1544898
• Dede, N. P. (2019). A study on the mediating role of trust for the leader in the relationship between transformational leadership and organizational commitment. Business & Management Studies: An International Journal, 7(4), 1923-1943.http://dx.doi.org/10.15295/bmij.v7i4.1250
• Deng, Y. (2024). A systematic review of application of machine learning in curriculum design among higher education. Journal of Emerging Computer Technologies, 4(1), 15-24. https://doi.org/10.57020/ject.1475566
• Erol, M., Canbeldek Erol, M., Erol, A., & Gök Çolak, F. (2025). Exploring the relationship between teachers' AI attitudes, AI self‐efficacy, and AI technological pedagogical content knowledge. European Journal of Education, 60(4), e70332.https://doi.org/10.1111/ejed.70332
• Fanaturiza, Y. A., & Rindaningsih, I. (2024). TPACK and teachers’ digital competence in the era of industry 40. International Journal Multidisciplinary (IJMI), 1(1), 16-23.
• Fornell, C., & Larcker, D. (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
• Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024). A scholarly definition of artificial intelligence (AI): Advancing AI as a conceptual framework in communication research. Political Communication, 41(2), 317-334. https://doi.org/10.1080/10584609.2023.2290497
• Goldman, S. R., Carreon, A., & Smith, S. J. (2024). Exploring the Integration of Artificial Intelligence into Special Education Teacher Preparation through the TPACK Framework. Journal of Special Education Preparation, 4(2), 52-64.
• Göçen, A., & Aydemir, F. (2020). Artificial intelligence in education and schools. Research on Education and Media, 12(1), 13-21.
• Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
• Harry, A. (2023). Role of AI in education. Interdiciplinary Journal & Hummanity (INJURITY), 2(3).
• Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2014). Exploratory factor analysis. Multivariate data analysis. Prentice Hall.
• 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
• Hojeij, Z., Kuhail, M. A., & ElSayary, A. (2024). Investigating in-service teachers’ views on ChatGPT integration. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-04-2024-0094
• İncemen, S., & Öztürk, G. (2024). Artificial intelligence in various educational areas: Application examples. International Journal of Computers in Education, 7(1), 27-49.https://doi.org/10.5281/zenodo.12600022
• İşler, B., & Kılıç, M. (2021). The use and development of artificial intelligence in education. New Media Electronic Journal, 5(1), 1-11.
• Kabakci Yurdakul, I., Odabasi, H.F., Kilicer, K., Coklar, A.N., Birinci, G. & Kurt, A.A. (2012). The development, validity and reliability of TPACK-deep: A Technological Pedagogical Content Knowledge scale, Computers & Education 58(3), 964–977. https://doi.org/10.1016/j.compedu.2011.10.012
• Karagöz, Y. (2021). Bilimsel araştırma yöntemleri ve yayın etiği (3.Baskı). Nobel.
• Karasar, N. (2012). Bilimsel araştırma yöntemi. Nobel Yayınevi.
• Karataş, F., & Ataç, B. A. (2025). When TPACK meets artificial intelligence: Analyzing TPACK and AI-TPACK components through structural equation modelling. Education and Information Technologies, 30(7), 8979-9004.https://doi.org/10.1007/s10639-024-13164-2
• Kaya, Z., Kaya, O. N., & Emre, İ. (2013). Teknolojik pedagojik alan bilgisi (TPAB) ölçeği’nin Türkçeye uyarlanması. Educational Sciences: Theory & Practice, 13(4). 2355- 2377.
• Kim, S. W. (2024). Development of a TPACK Educational Program to Enhance Pre-service Teachers' Teaching Expertise in Artificial Intelligence Convergence Education. International Journal on Advanced Science, Engineering & Information Technology, 14(1).
• Kline, R. B. (2011). Principles and practice of structural equation modeling. NY: The Guilford Press.
• Lan, Y. (2024). Through tensions to identity-based motivations: Exploring teacher professional identity in Artificial Intelligence-enhanced teacher training. Teaching and Teacher Education, 151, 104736. https://doi.org/10.1016/j.tate.2024.104736
• Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022, March). Empowering teachers with AI: Co-designing a learning analytics tool for personalized instruction in the science classroom. In LAK22: 12th international Learning Analytics and Knowledge Conference (pp. 1-12).
• Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
• Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(1), 4. https://doi.org/10.1186/s41239-022-00372-4
• 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
• Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of pre-service teachers towards artificial intelligence (AI): The role of AI-related teacher training courses and AI-TPACK within the technology acceptance model. Education Sciences, 15(2), 167.
• Sarıçam, H., & Günaydın, N. (2024). The adaptation of the digital stress scale for university students to Turkish: A validity and reliability study. TÜBA Higher Education Research/Review, 14(3), 11-24. https://doi.org/10.53478/yuksekogretim.1381953
• Setiyawan, A., Soeharto, S., Wijaya, T. T., Korenova, L., & Lavicza, Z. (2025). Measuring Teachers' Competencies for AI Integration: Development and Validation of the AI-TPACK in Vocational Education. Computers and Education Open, 100319.https://doi.org/10.1016/j.caeo.2025.100319
• Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11
• Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. Allyn and Bacon
• Türk, N., Batuk, B., Kaya, A., & Yıldırım, O. (2025). What makes university students accept generative artificial intelligence? A moderated mediation model. BMC Psychology, 13(1), 1-13. https://doi.org/10.1186/s40359-025-03559-2
• Xu, G., Yu, A., Gao, A., & Trainin, G. (2025). Developing an AI-TPACK framework: exploring the mediating role of AI attitudes in pre-service TCSL teachers’ self-efficacy and AI-TPACK. Education and Information Technologies, 30 22471–22495. https://doi.org/10.1007/s10639-025-13630-5
• Yang, Y., Xia, Q., Liu, C., & Chiu, T. K. (2025). The impact of TPACK on teachers’ willingness to integrate generative artificial intelligence (GenAI): The moderating role of negative emotions and the buffering effects of need satisfaction. Teaching and Teacher Education, 154, 104877. https://doi.org/10.1016/j.tate.2024.104877