YAPAY ZEKA KABUL ÖLÇEĞİ KISA FORMU’NUN PSİKOMETRİK ÖZELLİKLERİNİN İNCELENMESİ

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.

Keywords

• Yapay Zeka
• Yapay Zeka Kabulü
• Eğitimde Yapay Zeka

EXAMINING THE PSYCHOMETRIC PROPERTIES OF THE SHORT FORM ARTIFICIAL INTELLIGENCE ACCEPTANCE SCALE

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.

Keywords

• Artificial Intelligence
• AI Acceptance
• Artificial Intelligence in Education

References

1. Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206
2. Avcı, Ü. (2024). Students’ GAI acceptance: role of demographics, creative mindsets, anxiety, attitudes. Journal of Computer Information Systems, 1-15. https://doi.org/10.1080/08874417.2024.2386545
3. 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
4. Becker, D. (2019). Possibilities to improve online mental health treatment: recommendations for future research and developments. In Advances in Information and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 1 (pp. 91-112). Springer International Publishing.
5. Boxleitner, A. (2023). Integrating AI in education: opportunities, challenges and responsible use of ChatGPT. Education: Opportunities, Challenges and Responsible Use of ChatGPT (September 9, 2023).
6. Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: the sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 1-10. https://doi.org/10.1007/s12144-025-07433-7
7. Chai, C. S., Yu, D., King, R. B., & Zhou, Y. (2024). Development and validation of the artificial intelligence learning intention scale (AILIS) for university students. Sage Open, 14(2), 21582440241242188. https://doi.org/10.1177/21582440241242188
8. Chalita, M. A., & Sedzielarz, A. (2023). Beyond the frame problem: what (else) can Heidegger do for AI?AI & Society, 38(1), 173-184. https://doi.org/10.1007/s00146-021-01280-3

9. Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3),464–504. 10.1080/10705510701301834
10. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278. 10.1109/ACCESS.2020.2988510 Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 39(9), 1727-1739. https://doi.org/10.1080/10447318.2022.2050543
11. Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2018). Sosyal bilimler için çok değişkenli istatistik SPSS ve LISREL uygulamaları (5th ed.). Pegem.
12. 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
13. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
14. 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.
15. DeVellis, R.F. (2017). Scale Development: Theory and Applications. Sage Publications.
16. 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
17. 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
18. 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
19. Gürbüz, S. (2019). AMOS ile yapısal eşitlik modellemesi. Seçkin Yayıncılık
20. 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
21. Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2014). Exploratory Factor Analysis. Multivariate Data Analysis. Prentice Hall.
22. 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
23. 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
24. Hu, K., & Hu, K. (2023). ChatGPT sets record for fastest-growing user base—analyst note. Reuters, 12, 2023.
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
33. 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
34. 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
35. 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
36. McKinsey & Company. (2023). What is generative AI? From https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
37. Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856
38. Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D.J., Malhotra, N., Cai, J.C., Malhotra, N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med. Ethics 22 (1), 1–17. https://doi.org/10.1186/s12910-021-00577-8
39. Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020
40. Raffaghelli, J. E., Rodriguez, M. E., Guerrero-Roldan, A. E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182(2022), 104468. https://doi.org/10.1016/j.compedu.2022.104468
41. Raman, R., Mandal, S., Das, P., Kaur, T., JP, S., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study.
42. Ramu, M.M., Shaik, N., Arulprakash, P., Jha, S.K., & Nagesh, M.P. (2022). Study on potential AI applications in childhood education. Int. J. Early Childhood 14 (03). https://doi.org/10.9756/INT-JECSE/V14I3.1215
43. Rane, N., Choudhary, S. P., & Rane, J. (2024). Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. Journal of Applied Artificial Intelligence, 5(2), 50-70.
44. Schmidt, A., Giannotti, F., Mackay, W., Shneiderman, B., & Vaananen,K. (2021). Artificial intelligence for humankind: a panel on how to create truly interactive and human-centered AI for the benefit of individuals and society. IFIP Conference on Human-Computer Interaction.
45. Schwab, K. (2017). The Fourth Industrial Revolution. World Economic Forum.
46. Seçer, İ. (2015). Psikolojik test geliştirme ve uyarlama süreci: SPSS ve LISREL uygulamaları. Anı Yayıncılık.
47. Sedaghat, S. (2023). Success through simplicity: what other artificial intelligence applications in medicine should learn from history and ChatGPT. Annals of Biomedical Engineering, 51(12), 2657-2658.
48. Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE access, 11, 38805-38818.
49. Şata, M. (2020). Nicel araştırma yaklaşımları. Oğuz, E. (Eds.), Eğitimde araştırma yöntemleri içinde (p. 77–97). Eğiten Kitap Yayıncılık
50. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science 361 (6404), 751–752. https://doi.org/10.1126/science.aat5991.
51. Tamori, H., Yamashina, H., Mukai, M., Morii, Y., Suzuki, T., & Ogasawara, K. (2022). Acceptance of the use of artificial intelligence in medicine among Japan’s doctors and the public: a questionnaire survey. JMIR Human Factors, 9(1), e24680. doi:10.2196/24680
52. Tegmark, M. (2018). Life 3.0: Being human in the age of artificial intelligence. Vintage
53. 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
54. Theis, S., Jentzsch, S., Deligiannaki, F., Berro, C., Raulf, A. P., & Bruder, C. (2023). Requirements for explainability and acceptance of artificial intelligence in collaborative work. In International Conference on Human-Computer Interaction (pp. 355-380). Cham: Springer Nature Switzerland.
55. Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Inf. Softw. Technol. 52 (5), 463–479. https://doi.org/10.1016/j.infsof.2009.11.005.
56. Varzaru, A. A. (2022). Assessing artificial intelligence technology acceptance in managerial accounting. Electronics, 11(14), 2256. https://doi.org/10.3390/electronics11142256
57. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
58. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
59. Wang, H., & Wang, S. (2010). User acceptance of mobile internet based on the unified theory of acceptance and use of technology: Investigating the determinants and gender differences. Social Behavior and Personality, 38(3), 415–426. https://doi.org/10.2224/sbp.2010.38.3.415
60. Xia, W., Zhou, D., Xia, Q.Y., & Zhang, L.R. (2020). Design and implementation path of intelligent transportation information system based on artificial intelligence technology. J. Eng. 2020 (13), 482–485. https://doi.org/10.1049/joe.2019.1196
61. Yılmaz, F. G. K., Yılmaz, R., & Ceylan, M. (2024). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction, 40(24), 8703-8715. https://doi.org/10.1080/10447318.2023.2288730