Öğretmen Adaylarına Yönelik Yapay Zekâ Farkındalık Ölçeği / Artificial Intelligence Awareness Scale for Pre-Service Teachers

Öz

Bu çalışmanın amacı öğretmen adaylarının yapay zekâ (AI) farkındalığını değerlendirmek için geçerli ve güvenilir bir ölçüm aracı oluşturmaktır. Her ne kadar YZ teknolojileri giderek eğitim bağlamlarına entegre ediliyor olsa da literatür taraması, olumlu yönelimler ve kaygılar da dahil olmak üzere öğretmenlerin çok boyutlu farkındalığını ortaya koyan kapsamlı araçların hala eksik olduğunu göstermektedir. Genel olarak mevcut ölçekler ağırlıklı olarak teknik bilgi veya tutumlara odaklandığından, öğretmen adayları arasında bütünsel YZ farkındalığının ölçülmesine ilişkin bir boşluk doldurulamamıştır. Araştırma nicel tarama modelinin kullanıldığı bir ölçek geliştirme çalışmasıdır. Araştırmacılar, 2024-2025 eğitim-öğretim yılında gönüllü olarak bir devlet üniversitesine kayıt yaptıran 300 öğretmen adayından veri topladı. Kolayda örneklemeyi kullandılar. Başlangıçta, kapsamlı bir literatür taramasına ve uzman değerlendirmelerine dayanarak 71 madde bir araya getirildi. İçerik geçerliliği uzman bir panel aracılığıyla sağlandı ve yapı geçerliliği Açımlayıcı Faktör Analizi (EFA) ve Doğrulayıcı Faktör Analizi (CFA) aracılığıyla doğrulandı. İç tutarlılık güvenirliği Cronbach alfa katsayıları ile ölçülmüştür. EFA sonuçlarına göre maddelerin çıkarılmasının ardından 39 maddelik iki faktör tespit edilmiştir. Bu faktörler Yapay Zekâ Kabulü (26 madde) ve Yapay Zekâdan Kaçınma (13 madde) olarak yorumlandı. İki faktörlü model toplam varyansın yaklaşık %47,68'ini açıklamaktadır. CFA sonuçları modelin iyi uyum indekslerine sahip olduğunu doğrulamıştır. Ölçeğin geneli ve iki alt boyutu, güvenilirlik analizleriyle de doğrulanan, 0,89'un üzerindeki Cronbach alfa değerlerinin gösterdiği gibi çok iyi bir iç tutarlılığa sahipti. Çalışma bulguları, geliştirilen aracın öğretmen adayları arasında YZ farkındalığını değerlendirmek için psikometrik olarak geçerli bir araç olduğuna dair kanıt sunmaktadır. Ölçek yalnızca olumlu kabulü değil, aynı zamanda YZ farkındalığının çok boyutlu doğasını yansıtan kritik kaçınma yönlerini de içermektedir. Sonraki araştırmaların, ölçeği doğrulamak ve eğitimde YZ’nın sorumlu ve etkili kullanımını teşvik etmeye yönelik öğretmen eğitimi programlarını kolaylaştıracak bir araç olarak uygulamak için çeşitli örnekleri kullanması önerilmektedir.

Anahtar Kelimeler

• Yapay zekâ farkındalığı
• Öğretmen adayları
• Ölçek geliştirme
• Eğitim teknolojisi

Artificial Intelligence Awareness Scale for Pre-Service Teachers / Öğretmen Adaylarına Yönelik Yapay Zekâ Farkındalık Ölçeği

Öz

This study aimed to create a valid and reliable measurement tool to evaluate the awareness of artificial intelligence (AI) among pre-service teachers. Although AI technologies are progressively being integrated into educational contexts, the literature review shows that comprehensive instruments that reveal the teachers’ multidimensional awareness, including positive orientations and concerns, are still missing. Generally, existing scales are mainly focused on technical knowledge or attitudes, thus a gap in measuring holistic AI awareness among prospective teachers has remained unfilled. The research was a scale development study that used a quantitative survey design. The researchers gathered data from 300 pre-service teachers, who volunteered and were enrolled at a state university during the 2024-2025 academic year. They used convenience sampling. Initially, 71 items were pooled based on an extensive literature review and expert evaluations. Content validity was secured through an expert panel, and construct validity was verified by means of Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Internal consistency reliability was measured through Cronbach’s alpha coefficients. According to the EFA results, two factors with 39 items after the exclusion of the items were identified. These factors were interpreted as AI Acceptance (26 items) and AI Avoidance (13 items). The two-factor model accounted for about 47.68% of the total variance. CFA results verified that the model had good fit indices. The overall scale, as well as the two sub-dimensions, had very good internal consistency, as indicated by the Cronbach’s alpha values of more than .89, which were confirmed by reliability analyses. The study findings provide evidence that the developed instrument is a psychometrically valid tool for assessing the awareness of AI among pre-service teachers. The scale includes not only the positive acceptance but also the critical avoidance aspects reflecting the multidimensional nature of the AI awareness. It is suggested that subsequent research uses diverse samples to validate the scale and implement it as an instrument to facilitate teacher education programs committed to the promotion of the responsible and effective use of AI in education.

Anahtar Kelimeler

• Artificial intelligence awareness
• Pre-service teachers
• Scale development
• Educational technology

Destekleyen Kurum

The authors have not received any financial support for the research, authorship and/or publication of this article.

Etik Beyan

For this research, ethical permission was obtained from the decisions of Sivas Cumhuriyet University, Educational Sciences Research Ethics Board dated 28.11.2025 and numbered 642906.

Kaynakça

1. Akkaya, B., Özkan, A., & Özkan, H. (2021). Artificial intelligence anxiety (AIA) scale: Adaptation to Turkish, validity and reliability study. Alanya Academic View, 5(2), 1125–1146. https://doi.org/10.29023/alanyaakademik.833668
2. Banaz, E., & Demirel, O. (2024). Examining the artificial intelligence literacy of Turkish teacher candidates according to different variables. Dokuz Eylül University Buca Faculty of Education Journal, (60), 1516–1529. https://doi.org/10.53444/deubefd.1461048
3. Bayram, V. (2025). Artificial intelligence ethics: A scale development study. International Journal of Economic and Administrative Academic Research, 5(1), 123–148.
4. Büyüköztürk, Ş. (2024). Data analysis handbook for social sciences. Pegem Publishing.
5. Chen, X., Chen, R. R., Wei, S., & Davison, R. M. (2025). Herd behavior in social commerce: Understanding the interplay between self-awareness and environment-awareness. Internet Research, 35(3), 947–980. https://doi.org/10.1108/INTR-05-2022-0359
6. Chiu, T. K. (2021). A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends, 65(5), 796–807. https://doi.org/10.1007/s11528-021-00637-1
7. Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2022). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118
8. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.

9. Çağal, M. T., & Keskin, Y. M. (2024). Development of artificial intelligence and robot technology perception scale. Journal of Reasoning, 7(2), 138–157. https://doi.org/10.33817/muhakeme.1507672
10. Çam, M. B., Çelik, N., Turan Güntepe, E., & Durukan, Ü. G. (2021). Determining the awareness of prospective teachers about artificial intelligence technologies. Mustafa Kemal University Journal of Social Sciences Institute, 18(48), 263–285.
11. Çetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/TEM.2021.3138503
12. Darayseh, A. (2023). Acceptance of artificial intelligence in teaching: Science teachers' perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132
13. Demirdağ, S., Ünver, A. K., & Gürez, I. (2025). Development of a teacher artificial intelligence attitude scale: A validity and reliability study. Asian Studies, 9(33), 1–22. https://doi.org/10.31455/asya.1712885
14. DeVellis, R. F., & Thorpe, C. T. (2021). Scale development: Theory and applications. SAGE Publications.
15. Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241-6265. https://doi.org/10.1007/s10639-021-10627-8
16. Dick, S. (2019). Artificial Intelligence. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.92fe150c
17. Dolgikh, S. (2024). Self-awareness in natural and artificial intelligent systems: A unified information-based approach. Evolutionary Intelligence, 17(5), 4095–4114. https://doi.org/10.1007/s12065-024-00974-z
18. 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.
19. Endsley, M. R., & Jones, D. G. (2024). Situation awareness-oriented design: Review and future directions. International Journal of Human–Computer Interaction, 40(7), 1487–1504. https://doi.org/10.1080/10447318.2024.2318884
20. 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
21. Field, A. (2018). Discovering statistics using IBM SPSS statistics. SAGE Publications.
22. Gökçe-Tekin, Ö. (2025). Artificial intelligence literacy scale development and validation study. Western Anatolian Educational Sciences Journal, 16(1), 418–434. https://doi.org/10.51460/baebd.1609636
23. 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
24. Hasan, D. F., & Khidhir, A. M. (2023). Towards deep enhancement learning techniques using fuzzy logic: A survey. International Journal of Electrical and Computer Engineering (IJECE), 13(3), 3041–3055. http://doi.org/10.11591/ijece.v13i3.pp3041-3055
25. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31, 611–631. https://doi.org/10.1007/s40593-021-00239-1
26. How, M. L., & Hung, W. L. D. (2019). Educing AI-thinking in science, technology, engineering, arts, and mathematics (STEAM) education. Education Sciences, 9(3), 184. https://doi.org/10.3390/educsci9030184
27. İşler, B., & Kılıç, M. (2021). The use and development of artificial intelligence in education. New Media Electronics Journal, 5(1), 1–11. https://doi.org/10.17932/IAU.EJNM.25480200.2021/ejnm_v5i1001
28. Kalaycı, Ş. (2010). Multivariate statistical techniques with SPSS applications. Asil Publishing Distribution.
29. 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.
30. Karagöz, Y. (2016). Applied statistical analysis in SPSS 23 and AMOS 23. Nobel Academic Publishing.
31. Karaman, M. (2023). Exploratory and confirmatory factor analysis: A conceptual study. International Journal of Economics and Administrative Sciences, 9(1), 47–63.
32. Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy program for developing university students' conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology and Society, 26(1), 16–30.
33. Köklü, N. (2002). Açıklamalı istatistik terimleri sözlüğü. Nobel.
34. Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575.
35. Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021, March). Developing middle school students' AI literacy. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 191–197).
36. Lintner, A. (2024). A case study on critical thinking and artificial intelligence in middle school. Turkish Online Journal of Educational Technology-TOJET, 23(4), 1–7.
37. Luckin, R. (2018). Enhancing learning and teaching with technology: What the research says. UCL IOE Press.
38. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
39. Mahmoud, A. (2020). Artificial intelligence applications: An introduction to education development in the light of Corona virus pandemic COVID 19 challenges. International Journal of Research in Educational Sciences, 3(4), 171–224. https://doi.org/10.29009/ijres.3.4.4
40. Nazaretsky, T., Gökçay, D., & Silva, M. (2022). Preparing educators for an AI-driven world: Strategies for developing AI literacy in schools. Journal of Digital Learning in Teacher Education, 38(3), 223–237. https://doi.org/10.1234/jdlte.2022.38.3.223
41. Nuangchalerm, P., & Prachagool, V. (2023). AI-driven learning analytics in STEM education. International Journal of Research in STEM Education, 5(2), 77–84.
42. Owsley, C. S., & Greenwood, K. (2024). Awareness and perception of artificial intelligence operationalized integration in news media industry and society. AI & Society, 39(1), 417–431. https://doi.org/10.1007/s00146-022-01386-2
43. Özutku, R., & Başboğaoğlu, U. (2022). The scale of online learning perception: The COVID-19 effect on shifting higher education to distance learning in Turkey. E-Uluslararası Pedandragoji Dergisi, 2(1), 17–32.
44. Patton, M. Q. (2002). Qualitative research and evaluation methods. SAGE Publications.
45. Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminatory power, and respondent preferences. Acta Psychologica, 104(1), 1–15.
46. Ramazanoğlu, 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
47. Süleymanoğulları, M., Özdemir, A., & Tekin, A. (2024). Artificial intelligence scale: A validity and reliability study. Education Science and Sports, 6(1), 13–27.
48. Tavşancıl, E. (2014). Tutumların ölçülmesi ve SPSS ile veri analizi. Ankara, Turkey: Nobel Akademi Publishing.
49. Tekin, N. (2023). Artificial intelligence in education: A content analysis on the trends of research from Turkey. Necmettin Erbakan University Ereğli Faculty of Education Journal, 5(Special Issue), 387–411. https://doi.org/10.51119/ereegf.2023.49
50. Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 31(3).
51. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.
52. Wang, B., Rau, P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour and Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929x.2022.2072768
53. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
54. Xia, Q., Chiu, T. K., & Chai, C. S. (2023). The moderating effects of gender and need satisfaction on self-regulated learning through artificial intelligence (AI). Education and Information Technologies, 28(7), 8691–8713.
55. Yılmaz, Y., Yılmaz, D. U., Yıldırım, D., Korhan, E. A. & Kaya, D. Ö. (2021). Opinions of health sciences faculty students on artificial intelligence and the use of artificial intelligence in health. Suleyman Demirel University Journal of Health Sciences, 12(3), 297–308.
56. Zhao, L., Chen, L., Liu, Q., Zhang, M., & Copland, H. (2019). Artificial intelligence-based platform for online teaching management systems. Journal of Intelligent and Fuzzy Systems, 37(1), 45–51. https://doi.org/10.3233/JIFS-179062