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Journals / Erzincan Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Cilt: 28 Sayı: 2026

Adaptation of the Academic AI Usage Scale Into Turkish: A Validity and Reliability Study

Akademik Amaçlarla Yapay Zekâ Kullanım Ölçeği’nin Türkçeye Uyarlanması: Geçerlik ve Güvenirlik Çalışması

Şadiye Gürsoy(SELÇUK ÜNİVERSİTESİ)0009-0006-0809-1746
Hasan Bozgeyikli(SELÇUK ÜNİVERSİTESİ)0000-0002-6762-1990
Pages
1–15
Publication Date
2026-08-04
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The resulting 22-item model showed an overall acceptable level of fit (CFI = .906, TLI = .889, RMSEA = .074, SRMR = .067).

📌 3 Key Takeaways

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Objective & Hypothesis:This study aimed to adapt the Academic AI Usage Scale (AAIUS), developed to assess university students’ use of artificial intelligence tools for academic purposes, into Turkish and to examine preliminary evidence regarding its psychometric properties.
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Key Findings & Data:The study group consisted of 311 students enrolled at various higher education institutions in Turkey; following data quality checks, analyses were conducted on data from 264 participants.
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Conclusion & Impact:The resulting 22-item model showed an overall acceptable level of fit (CFI = .906, TLI = .889, RMSEA = .074, SRMR = .067).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Akademik yapay zekâ kullanımı · üretken yapay zekâ · üniversite öğrencileri

Abstract

This study aimed to adapt the Academic AI Usage Scale (AAIUS), developed to assess university students’ use of artificial intelligence tools for academic purposes, into Turkish and to examine preliminary evidence regarding its psychometric properties. The study group consisted of 311 students enrolled at various higher education institutions in Turkey; following data quality checks, analyses were conducted on data from 264 participants. Construct validity was examined through confirmatory factor analysis, and reliability was assessed using Cronbach’s alpha and McDonald’s omega coefficients. Because the initial model did not demonstrate adequate fit, modification indices, factor loadings, and item content were evaluated together; theoretically justified error covariances were specified between some item pairs within the same subscale, and two items with low factor loadings (M1 and M8) were removed. The resulting 22-item model showed an overall acceptable level of fit (CFI = .906, TLI = .889, RMSEA = .074, SRMR = .067). Reliability coefficients were satisfactory across all subscales (α = .811–.901; ω = .814–.905). Criterion-related validity analyses revealed moderate-to-high significant relationships between AAIUS scores and scores on the Generative AI Usage and Competence Scale. However, a limited discriminant validity concern was observed between the Academic Support and Academic Skills subscales. Overall, the findings suggest that the Turkish form of the AAIUS is a psychometrically promising instrument for assessing academic AI usage in higher education, although further testing of the 22-item structure in independent samples would be useful.

Özet

Bu araştırmada, üniversite öğrencilerinin yapay zekâ araçlarını akademik amaçlarla kullanım düzeylerini ölçmek üzere geliştirilmiş olan Akademik Yapay Zekâ Kullanım Ölçeği’ninTürkçeye uyarlanması amaçlanmış ve ölçeğin psikometrik özelliklerine ilişkin ön kanıtlar incelenmiştir. Araştırmanın çalışma grubunu Türkiye'deki çeşitli yükseköğretim kurumlarında öğrenim gören 311 öğrenci oluşturmuş; veri kalitesi kontrolleri sonucunda analizler 264 katılımcıdan elde edilen veriler üzerinden yürütülmüştür. Ölçeğin yapı geçerliği doğrulayıcı faktör analizi ile sınanmış, güvenirliği Cronbach alfa ve McDonald omega katsayılarıyla değerlendirilmiştir. Başlangıç modelinin uyum indekslerinin yeterli düzeyde olmaması üzerine modifikasyon indeksleri, faktör yükleri ve madde içerikleri birlikte değerlendirilmiş; aynı alt boyut içinde yer alan ve içerik bakımından yakınlık gösteren bazı madde çiftleri arasında kuramsal gerekçeye dayalı hata kovaryansları tanımlanmış ve düşük faktör yüküne sahip iki madde (M1 ve M8) ölçekten çıkarılmıştır. Elde edilen 22 maddeli son modelin genel olarak kabul edilebilir düzeyde uyum sergilediği görülmüştür (CFI = .906, TLI = .889, RMSEA = .074, SRMR = .067). Güvenirlik katsayıları tüm alt boyutlar için yeterli düzeyde bulunmuştur (α = .811–.901; ω = .814–.905). Ölçüt bağıntılı geçerlik kapsamında, Akademik Yapay Zekâ Kullanım Ölçeği puanlarının Üretken Yapay Zekâ Kullanım ve Yetkinlik Ölçeği puanlarıyla orta ile yüksek düzey arasındaanlamlı ilişkiler sergilediği belirlenmiştir. Bununla birlikte, Akademik Destek ile Akademik Beceriler boyutları arasında sınırlı düzeyde ayrışma geçerliği sorunu saptanmıştır. Bulgular, ölçeğin Türkçe formunun yükseköğretimde akademik amaçlı yapay zekâ kullanımını değerlendirmede kullanılabilecek, psikometrik açıdan umut verici bir araç olduğunu; ancak 22 maddelik yapının bağımsız örneklemlerde yeniden sınanmasının yararlı olacağını göstermektedir.

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Cite This Article

Gürsoy, Ş., Bozgeyikli, H. (2026). Adaptation of the Academic AI Usage Scale Into Turkish: A Validity and Reliability Study. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, Cilt: 28 Sayı: 2026, 1–15. https://doi.org/10.17556/erziefd.1929869

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References (46)

  1. Arslankara, V. B., & Usta, E. (2024). Generativeartificial intelligence as a lifelong learning self-efficacy: Usageandcompetencescale. Journal of Teacher Education and Lifelong Learning, 6(2), 288–302. https://doi.org/10.51535/tell.1489304
  2. Batuk, B., Aktu, Y., & Türk, N. (2025). Yapay zekâ kabul ölçeği kısa formu’nunpsikometrik özelliklerinin incelenmesi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Özel Sayı), 438–451. https://doi.org/10.35379/cusosbil.1695975
  3. Bayraktar, B., Gülderen, S., Akça, S., & Serin, E. (2023). Yapay zekâ teknolojilerinin eğitimde kullanımına yönelik öğretmen görüşleri. Ulusal Eğitim Dergisi, 3(11), 2012–2030. https://uleder.com/index.php/uleder/article/view/380
  4. Behling, O., &Law, K. S. (2000). Translating questionnaires and other research instruments: Problemsandsolutions. Sage.
  5. Beig, S., &Qasim, S. H. (2023). Artificial Intelligence Scale (1st ed.). National Psychological Corporation.
  6. Bozkurt, A., & Sharma, R. C. (2023). Challenging the statusquo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(1), i–viii. https://doi.org/10.5281/zenodo.7755273
  7. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S.,. & Jandrić, P. (2023). Speculative futures on Chat GPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568
  8. Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20(4), 872–882.
  9. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421
  10. Chakraborty, D., & Subramani, D. (2025). Development and validation of the Academic AI Usage Scale (AAIUS) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-7605472/v1
  11. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  12. Curran, P. G. (2016). Methods for the detection of carelessly invalid responses in survey data. Journal of Experimental Social Psychology, 66, 4–19. https://doi.org/10.1016/j.jesp.2015.07.006
  13. 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. https://doi.org/10.1111/bjop.12046
  14. Fornell, C., & Larcker, D. F. (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
  15. Gökçeaslan, Ş., Yıldız Durak, H., Gunbatar, M. S., Atman Uslu, N., & Elçi, A. N. (2025). Ethical and social risk awareness in generative artificial intelligence: The role of mindset and generative AI literacy. International Journal of Technology in Education, 8(4), 914–937.
  16. 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, Article 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
  17. Gruenhagen, J. H., Sinclair, P. M., Carroll, J.-A., Baker, P. R. A., Wilson, A., &Demant, D. (2024). Therapidrise of generative AI and its implications for academic integrity: Students’ perceptions and use of chatbots for assistance with assessments. Computers and Education: Artificial Intelligence, 7, Article 100273. https://doi.org/10.1016/j.caeai.2024.100273
  18. Gümüş, M. M., & Kara, M. (2025). Development and validation of the Generative AI Literacy for Learning Scale (Gen AI-LLs). Australasian Journal of Educational Technology, 41(4), 1–16. https://doi.org/10.14742/ajet.10236
  19. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
  20. Hambleton, R. K., Merenda, P. F., & Spielberger, C. D. (Eds.). (2005). Adapting educational and psychological tests for cross-cultural assessment. Lawrence Erlbaum Associates.
  21. Hambleton, R. K., &Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Applied Testing and Technology, 1(1), 1–16.
  22. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  23. International Test Commission. (2017). The ITC guidelines for translating and adapting tests (2nd ed.). https://www.intestcom.org/files/guideline_test_adaptation_2ed.pdf
  24. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  25. Komsta, L., & Novomestky, F. (2022). moments: Moments, cumulants, skewness, kurtosis and related tests (R package version 0.14.1). https://CRAN.R-project.org/package=moments
  26. Mc Donald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
  27. Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. https://doi.org/10.1037/a0028085
  28. Micabalo, A., Oanga, A., Ormita, E., Orola, G., Ong, J. D., & Fuentes, J. (2024). The impact of AI-powered study tools on the academic performance and motivation of higher education students [Undergraduate thesis, Xavier University – Ateneo de Cagayan].
  29. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). Mc Graw-Hill.
  30. R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
  31. Revelle, W. (2026). psych: Procedures for psychological, psychometric, and personality research (R package version 2.6.3). https://cran.gedik.edu.tr/web/packages/psych/psych.pdf
  32. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
  33. Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023). Assessing health students’ attitudes and usage of Chat GPT in Jordan: Validation study. JMIR Medical Education, 9, Article e48254. https://doi.org/10.2196/48254
  34. Süleymanoğulları, M., Özdemir, A., & Tekin, A. (2025). Yapay Zekâ Ölçeği: Geçerlik ve güvenirlik çalışması. Education Science and Sports, 7(1), 58-58. https://izlik.org/JA87CA26MH
  35. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
  36. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
  37. Uluğ, E., Öner, K., Arslantaş, S., & Harmancı, S. T. (2025). Adaptation of the artificial intelligence literacy scale into Turkish: A cross-sectional application among healthcare workers, students, and children. Education and Information Technologies, 30, 21041–21077. https://doi.org/10.1007/s10639-025-13597-3
  38. Ulusal Yapay Zekâ Stratejisi. (2021). 2021–2025 Ulusal Yapay Zekâ Stratejisi. T.C. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi.
  39. Ward, B., Bhati, D., Neha, F., & Guercio, A. (2024). Analyzing the impact of AI tools on student study habits and academic performance [Preprint]. ar Xiv. https://doi.org/10.48550/arXiv.2412.02166
  40. Ward, M. K., & Meade, A. W. (2023). Dealing with careless responding in survey data. Annual Review of Psychology, 74, 577–596. https://doi.org/10.1146/annurev-psych-040422-045007
  41. Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer. https://doi.org/10.1007/978-3-319-24277-4
  42. Wickham, H., & Bryan, J. (2023). readxl: Read Excel files (R package version 1.4.3).
  43. Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A grammar of data manipulation (R package version 1.1.4).
  44. Yaman, S., Korkmaz, Ö., Özdemir, S. M., Akbaş, O., Çakır, R., Usta, E., & Tokcan, H. (2026). Lisans öğrencileri için üretken yapay zekâ araçlarının akademik amaçlı kullanım eğilimi ölçeği: Geçerlik ve güvenirlik çalışması. Gazi Eğitim Bilimleri Dergisi, 12(1), 120–151. https://izlik.org/JA52CW88ZP
  45. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(2024), Article 28. https://doi.org/10.1186/s40561-024-00316-7
  46. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S.,. & Jandrić, P. (2023). Speculative futures on Chat GPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568