YAPAY ZEKA KABUL ÖLÇEĞİ KISA FORMU’NUN PSİKOMETRİK ÖZELLİKLERİNİN İNCELENMESİ
Year 2025,
Volume: 34 Issue: Uygarlığın Dönüşümü: Yapay Zekâ, 438 - 451, 20.07.2025
Barzan Batuk
,
Yahya Aktu
,
Nuri Türk
Abstract
Eğitimde yapay zeka alanındaki araştırmaların merkezinde yapay zeka kabulü bulunmaktadır. Özellikle yüksek öğrenimde yapay zeka entagrasyonunun sağlanması, yapay zeka kabulüne bağlıdır. Bu çalışmada Yapay Zeka Kabul Ölçeği Kısa Formu’nu psikometrik özelliklerinin incelenerek Türkçe’ye kazandırılması amaçlanmıştır. Kolayda örneklem yöntemi kullanılan çalışmanın örneklemini 205 üniversite öğrencisi oluşturmaktadır. Çalışmanın veri toplama araçları arasında Yapay Zeka Kabul Ölçeği Kısa Formu ile Yapay Zeka Güncel Öğrenimi ölçekleri bulunmaktadır. Yapılan güvenirlik analizi sonuçlarına göre Yapay Zeka Kabul Ölçeği Kısa Formu’nun Cronbach α = 0.76 ve McDonald’s ω = 0.72 olduğu görülmüştür. Araştırma bulguları ölçeğin madde ayırt edicilik indeks değerlerinin ve madde faktör yüklerinin iyi düzeyde olduğunu göstermiştir.Ölçeğin DFA sonuçları, model uyum değerlerinin mükemmel düzeyde olduğunu göstermiştir. Bununla birlilkte, ölçeğin yakınsak ıraksak geçerliliğinin sağladığını kanıtlamıştır. Ölçüt geçerliliği ile ilgili yapılan analizlere göre, yapay zeka kabulü ile yapay zekanın güncel öğrenimi arasında anlamlı pozitif ilişkilere sahiptir. Araştırma sonuçları, Yapaz Zeka Kabul Ölçeği Kısa Formu’nun Türkiye örnekleminde geçerli ve güvenilir bir ölçme aracı olarak kullanılabileceğini ortaya koymuştur.
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EXAMINING THE PSYCHOMETRIC PROPERTIES OF THE SHORT FORM ARTIFICIAL INTELLIGENCE ACCEPTANCE SCALE
Year 2025,
Volume: 34 Issue: Uygarlığın Dönüşümü: Yapay Zekâ, 438 - 451, 20.07.2025
Barzan Batuk
,
Yahya Aktu
,
Nuri Türk
Abstract
Artificial intelligence (AI) acceptance is at the core of research in the field of artificial intelligence in education. Especially in higher education, ensuring the integration of AI depends on the level of acceptance among users. This study aims to adapt the Artificial Intelligence Acceptance Scale Short Form into Turkish and examine its psychometric properties. The sample of the study, in which convenience sampling method was used, consisted of 205 university students. The data collection tools of the study include Artificial Intelligence Acceptance Scale Short Form and Artificial Intelligence Current Learning scales. According to the results of the reliability analysis, Cronbach α = 0.76 and McDonald's ω = 0.72 for the Artificial Intelligence Acceptance Scale Short Form, indicating satisfactory internal consistency. The research findings showed that the item discrimination index values and item factor loadings of the scale were at acceptable levels. Confirmatory factor analysis (CFA) results revealed revealed excellent model fit indices. Moreover, the scale demonstrated both convergent and discriminant validity.. According to the analyses of criterion validity, there are significant positive relationships between AI acceptance and current AI learning. The results of the study revealed that the Turkish version of the Artificial Intelligence Acceptance Scale Short Form is a valid and reliable measurement tool for assessing AI acceptance among university students in Turkey.
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Baytak, A. (2023). The acceptance and diffusion of generative artificial intelligence in education: A literature review. Current Perspectives in Educational Research, 6(1), 7-18. https://doi.org/10.46303/cuper.2023.2
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-
Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2018). Sosyal bilimler için çok değişkenli istatistik SPSS ve LISREL uygulamaları (5th ed.). Pegem.
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Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024). Exploring the influence of ChatGPT on student academic success and career readiness. Education and Information Technologies, 1-45. https://doi.org/10.1007/s10639-024-13148-2
-
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
-
Davis, F.D.(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340. https://doi.org/10.2307/249008.
-
DeVellis, R.F. (2017). Scale Development: Theory and Applications. Sage Publications.
-
De Winter, J., Dodou, D., & Eisma, Y. B. (2024). Personality and acceptance as predictors of ChatGPT use. Discover Psychology, 4(1), 57. https://doi.org/10.1007/s44202-024-00161-2
-
Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676-5696. https://doi.org/10.1080/00207543.2023.2294116
-
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
-
Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi. Seçkin Yayıncılık
-
Hagendorff, T., & Wezel, K. (2020). 15 challenges for AI: or what AI (currently) can’t do. AI & Society, 35(2), 355-365. https://doi.org/10.1007/s00146-019-00886-y
-
Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2014). Exploratory Factor Analysis. Multivariate Data Analysis. Prentice Hall.
-
Harshvardhan, G. M., Gourisaria, M. K., Pandey, M., & Rautaray, S. S. (2020). A comprehensive survey and analysis of generative models in machine learning. Computer Science Review, 38(2020), 100285. https://doi.org/10.1016/j.cosrev.2020.100285
-
Hernández, A., Hidalgo, M. D., Hambleton, R. K., & Gómez Benito, J. (2020). International test commission guidelines for test adaptation: A criterion checklist. Psicothema, 32(3), 390–398. https://doi.org/10.7334/psicothema2019.306
-
Hu, K., & Hu, K. (2023). ChatGPT sets record for fastest-growing user base—analyst note. Reuters, 12, 2023.
-
Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
-
Hwang, G. J., Xie, H., Wah, B. W., & 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
-
Kahn, K., & Winters, N. (2021). Constructionism and AI: A history and possible futures. British Journal of Educational Technology, 52(3), 1130-1142. https://doi.org/10.1111/bjet.13088
-
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
-
Kashive, N., Powale, L., & Kashive, K. (2021). Understanding user perception toward artificial intelligence (AI) enabled e-learning. Int. J. Inf. Learn. Technol. 38 (1), 1–19. https://doi.org/10.1108/IJILT-05-2020-0090
-
Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis & Prevention, 137, 105441. https://doi.org/10.1016/j.aap.2020.105441
-
Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77(2023), 101925. https://doi.org/10.1016/j.tele.2022.101925
-
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
-
Lieto, A., Bhatt, M., Oltramari, A., & Vernon, D. (2018). The role of cognitive architectures in general artificial intelligence. Cognitive Systems Research, 48(2018), 1–3. https://doi.org/10.1016/j.cogsys.2017.08.003
-
Long, X., Deng, H., Zhang, Z., Liu, T., Yu, X., Gong, P., & Tian, L. (2023). Development and evaluation of acceptance scale for artificial intelligence in digestive endoscopy by subjects. Zhong nan da xue xue bao. Yi xue ban= Journal of Central South University. Medical Sciences, 48(12), 1844-1853. https://doi.org/10.11817/j.issn.1672-7347.2023.230225
-
Matsika, C., & Zhou, M. (2021). Factors affecting the adoption and use of AVR technology in higher and tertiary education. Technology in Society, 67, 101694. https://doi.org/10.1016/j.techsoc.2021.101694
-
McKinsey & Company. (2023). What is generative AI? From https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
-
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