Artificial Intelligence Literacy Scale: Turkish Adaptation Study

Öz

This study aims to adapt the Artificial Intelligence Literacy Scale developed by Ng et al. (2024) into Turkish. In this direction, the original scale was translated into Turkish by taking expert opinions. The translated scale was applied to 734 middle and high school students. In the scale adaptation process, firstly, the construct validity was tested by applying confirmatory factor analysis (CFA) on the existing factor structure of the original scale. The analyses showed that the original scale structure was incompatible with Turkish culture. After, exploratory factor analysis (EFA) was conducted to determine the scale’s factor structure appropriate for Turkish culture, and the new factor structure was examined. Then, first-order confirmatory factor analysis (CFA) was applied to the resulting factor structure, and the model fit indices were calculated as CFI = 0.913, TLI = 0.902, RMSEA = 0.056, and SRMR = 0.065 with the corrections made according to the modification suggestions. The second level CFA was applied to the model, and the model fit indices were CFI = 0.908, TLI = 0.901, RMSEA = 0.058, and SRMR = 0.072, with the corrections made according to the modification indices. To determine the reliability of the scale, Cronbach’s alpha (α) and McDonald’s omega (ω) values were calculated and α = 0.909 and ω = 0.902. The results show that the scale adapted to Turkish is a valid and reliable measurement tool for assessing the AI literacy levels of middle and high school students. This study is thought to contribute significantly to the studies on artificial intelligence in Türkiye. It is also believed to be a guide for scale adaptation studies.

Anahtar Kelimeler

• artificial intelligence
• artificial intelligence literacy
• scale adaptation

Yapay Zekâ Okuryazarlığı Ölçeği: Türkçeye Uyarlama Çalışması

Öz

Bu araştırmanın amacı, Ng ve diğerleri (2024) tarafından geliştirilen Yapay Zekâ Okuryazarlığı Ölçeğinin Türkçeye uyarlanmasıdır. Bu doğrultuda uzman görüşleri alınarak orijinal ölçeğin Türkçeye tercümesi yapılmıştır. Tercümesi yapılan ölçek ortaokul ve lise düzeyinde öğrenim gören 734 öğrenciye uygulanmıştır. Ölçek uyarlama sürecinde öncelikle orijinal ölçeğin mevcut faktör yapısı üzerinde doğrulayıcı faktör analizleri (DFA) uygulanarak yapı geçerliği test edilmiştir. Analizler sonucunda elde edilen bulgular, orijinal ölçek yapısının Türk kültürü ile uyumlu olmadığını göstermiştir. Sonrasında ölçeğin Türkçeye uygun olan faktör yapısının belirlenmesi amacıyla açımlayıcı faktör analizi (AFA) yapılarak yeni faktör yapısı incelenmiştir. Ortaya çıkan faktör yapısı üzerinde birinci düzey DFA uygulanmış ve modifikasyon önerilerine göre yapılan düzeltmeler ile model uyum indeksleri CFI = 0.913, TLI = 0.902, RMSEA = 0.056 ve SRMR = 0.065 olarak hesaplanmıştır. Model üzerinde ikinci düzey DFA uygulanmış ve modifikasyon indekslerine göre yapılan düzeltmeler ile model uyum ölçütleri CFI = 0.908, TLI = 0.901, RMSEA = 0.058 ve SRMR = 0.072 olarak elde edilmiştir. Ölçeğin güvenilirliğini belirlemek için Cronbach’s alpha (α) ve McDonald’s omega (ω) değerleri hesaplanmış ve α = 0.909 ve ω = 0.902 olarak bulunmuştur. Sonuçlar, Türkçeye uyarlanan ölçeğin ortaokul ve lise düzeyindeki öğrencilerin yapay zekâ okuryazarlık düzeylerini değerlendirmek için geçerli ve güvenilir bir ölçme aracı olduğunu göstermektedir. Bu çalışmanın Türkiye’de yapay zekâ ile ilgili çalışmalara önemli bir katkı sağlayacağı düşünülmektedir. Ayrıca ölçek uyarlama çalışmaları için yol gösterici nitelikte olacağına inanılmaktadır.

Anahtar Kelimeler

• ölçek uyarlama
• yapay zekâ
• yapay zekâ okuryazarlığı

Kaynakça

1. Akbaş, G. ve Korkmaz, L. (2007). Ölçek uyarlaması (Adaptasyon). Türk Psikoloji Bülteni, 13(40), 15–16. https://doi.org/10.31828/tpb134003
2. Byrne, B. M., & Van de Vijver, F. J. (2010). Testing for measurement and structural equivalence in large-scale cross-cultural studies: Addressing the issue of nonequivalence. International Journal of Testing, 10(2), 107-132.
3. Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
4. Chiu, T. K. F., Chen, Y., Yau, K. W., Chai, C.-S., Meng, H., King, I., Wong, S., & Yam, Y. (2024). Developing and validating measures for AI literacy tests: From self-reported to objective measures. Computers and Education: Artificial Intelligence, 7, 100282. https://doi.org/10.1016/j.caeai.2024.100282
5. Chiu, T. K. F., Meng, H., Chai, C.-S., King, I., Wong, S., & Yam, Y. (2022). Creation and evaluation of a pretertiary artificial intelligence (AI) curriculum. IEEE Transactions on Education, 65(1), 30–39. https://doi.org/10.1109/TE.2021.3085878
6. Choi, C. H., & You, Y. Y. (2017). The study on the comparative analysis of EFA and CFA. Journal of Digital Convergence, 15(10), 103–111. https://doi.org/10.14400/JDC.2017.15.10.103
7. Çelebi, C., Yilmaz, F., Demi̇r, U. ve Karakuş, F. (2023). Yapay zekâ okuryazarlığı: Bir ölçek uyarlama çalışması. Instructional Technology and Lifelong Learning, 4(2), 291–306. https://doi.org/10.52911/itall.1401740
8. 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

9. DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Thousand Oaks, CA: Sage. Ergül, B., & Yıldız, Z. (2023). Comparison of classical and robust factor analyses methods. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 27(3), 401–410. https://doi.org/10.19113/sdufenbed.1250855
10. Erkuş, A. (2007). Ölçek geliştirme ve uyarlama çalışmalarında karşılaşılan sorunlar. Türk Psikoloji Bülteni, 13(40), 17–25. https://doi.org/10.31828/tpb134004
11. Erkutlu, H., Erdemir Ergün, E., Köseoğlu, İ. ve Vurgun, T. (2023). Yapay zekâ ve örgütsel davranış. Nevşehir Sosyal Bilimler Enstitüsü Dergisi, 13(3), 1403–1417. https://doi.org/10.30783/nevsosbilen.1246678
12. Gökçearslan, Ş., Durak, H., Günbatar, M., Atman Uslu, N., & Elçi, A. (2024, July 19–20). Generative artificial intelligence (GenAI) literacy scale: Validity and reliability [Conference presentation]. 4th International Conference on Scientific and Academic Research, Konya, Türkiye.
13. Gökoğlu, S. ve Çakıroğlu, Ü. (2019). Sanal gerçeklik temelli öğrenme ortamlarında bulunuşluk hissinin ölçülmesi: Bulunuşluk ölçeğinin Türkçe’ye uyarlanması. Eğitim Teknolojisi Kuram ve Uygulama, 9(1), 169–188. https://doi.org/10.17943/etku.441497
14. Gökoğlu, S., & Erdoğdu, F. (2025). The effects of GenAI on learning performance: A meta-analysis study. Educational Technology & Society, 28(3). https://doi.org/10.30191/ETS.202507_28(3).TP04
15. Green, J. P., Tonidandel, S., & Cortina, J. M. (2016). Getting through the gate: Statistical and methodological issues raised in the reviewing process. Organizational Research Methods, 19(3), 402–432. https://doi.org/10.1177/1094428116631417
16. Hobeika, E., Hallit, R., Malaeb, D., Sakr, F., Dabbous, M., Merdad, N., ... & Fekih-Romdhane, F. (2024). Multinational validation of the Arabic version of the Artificial Intelligence Literacy Scale (AILS) in university students. Cogent Psychology, 11(1), 2395637. https://doi.org/10.1080/23311908.2024.2395637
17. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.
18. Jin, K.-Y., Reichert, F., Cagasan, L. P., de la Torre, J., & Law, N. (2020). Measuring digital literacy across three age cohorts: Exploring test dimensionality and performance differences. Computers & Education, 157, 103968. https://doi.org/10.1016/j.compedu.2020.103968
19. Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October 12–15). Artificial intelligence and computer science in education: From kindergarten to university [Conference presentation]. IEEE Frontiers in Education Conference, Erie, PA, USA. https://doi.org/10.1109/FIE.2016.7757570
20. Karaoğlan Yılmaz, F. G. ve Yılmaz, R. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçeye uyarlanması. Bilgi ve İletişim Teknolojileri Dergisi, 5(2), 172–190. https://doi.org/10.53694/bited.1376831
21. Kim, M., & Choi, D. (2018). Development of youth digital citizenship scale and implication for educational setting. Journal of Educational Technology & Society, 21(1), 155–171. https://www.jstor.org/stable/26273877
22. Kit Ng, D. T., Wu, W., Lok Leung, J. K., & Wah Chu, S. K. (2023, July 10–13). Artificial intelligence (AI) literacy questionnaire with confirmatory factor analysis [Conference presentation]. IEEE International Conference on Advanced Learning Technologies, Orem, Utah, United States. https://doi.org/10.1109/ICALT58122.2023.00074
23. Koch, M. J., Wienrich, C., Straka, S., Latoschik, M. E., & Carolus, A. (2024). Overview and confirmatory and exploratory factor analysis of AI literacy scale. Computers and Education: Artificial Intelligence, 7, 100310. https://doi.org/10.1016/j.caeai.2024.100310
24. Kolenikov, S., & Bollen, K. A. (2012). Testing negative error variances: Is a Heywood case a symptom of misspecification? Sociological Methods & Research, 41(1), 124–167. https://doi.org/10.1177/0049124112442138
25. Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 26(1), 16–30. https://www.jstor.org/stable/48707964
26. Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports, 12, 100338. https://doi.org/10.1016/j.chbr.2023.100338
27. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
28. Lee, S., & Park, G. (2024). Development and validation of ChatGPT literacy scale. Current Psychology, 43, 18992–19004. https://doi.org/10.1007/s12144-024-05723-0
29. Lin, P. Y., Chai, C. S., Jong, M. S. Y., Dai, Y., Guo, Y., & Qin, J. (2021). Modeling the structural relationship among primary students’ motivation to learn artificial intelligence. Computers and Education: Artificial Intelligence, 2, 100006. https://doi.org/10.1016/j.caeai.2020.100006
30. Long, D., & Magerko, B. (2020, April 25–30). What is AI literacy? Competencies and design considerations [Conference presentation]. CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA. https://doi.org/10.1145/3313831.3376727
31. Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w
32. Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
33. Polatgil, M. ve Güler, A. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçe’ye uyarlanması. Sosyal Bilimlerde Nicel Araştırmalar Dergisi, 3(2), 99–114. https://doi.org/10.53694/bited.1376831
34. Su, J. (2024). Development and validation of an artificial intelligence literacy assessment for kindergarten children. Education and Information Technologies, 29, 21811–21831. https://doi.org/10.1007/s10639-024-12611-4
35. Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9795-9799. https://doi.org/10.1609/aaai.v33i01.33019795
36. UNESCO. (2022). K-12 AI curricula: A mapping of government-endorsed AI curricula (ED-2022/FLI-ICT/K-12 REV.). https://unesdoc.unesco.org/ark:/48223/pf0000380602
37. Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
38. Yue, M., Jong, M. S. Y., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: A systematic review. Sustainability, 14(23), 15620. https://doi.org/10.3390/su142315620
39. Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2023). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33, 290–324. https://doi.org/10.1007/s40593-022-00293-3