SavedAdd your journal

Journals / International Journal of Assessment Tools in Education / 2026 / Volume: 13 Issue: 3

Developing an attitude scale for preservice teachers towards artificial intelligence technology: A validity and reliability study

Burcu Alan(FIRAT ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0000-0003-3429-0942
Fikriye Kırbağ Zengin(FIRAT ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0000-0002-0547-8746
Gonca Keçeci(FIRAT ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0000-0002-2582-3850
Pages
—
Publication Date
2026-09-02
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

These findings indicate that the scale is both valid and reliable, providing a robust tool for measuring preservice teachers' attitudes toward AI technology.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aimed to develop a measurement tool to assess preservice teachers' attitudes toward artificial intelligence technology.
📊
Key Findings & Data:The study sample was selected using a convenient sampling method and comprised 672 preservice teachers from various departments and grade levels in the Faculty of Education of a state university.
💡
Conclusion & Impact:These findings indicate that the scale is both valid and reliable, providing a robust tool for measuring preservice teachers' attitudes toward AI technology.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · Reliability · Validity

Abstract

This study aimed to develop a measurement tool to assess preservice teachers' attitudes toward artificial intelligence technology. In this study, a cross-sectional design was employed within the scope of quantitative research. The study sample was selected using a convenient sampling method and comprised 672 preservice teachers from various departments and grade levels in the Faculty of Education of a state university. The scale development process has been carried out systematically. A pool of 70 items was generated from the relevant literature and formatted as a Likert-type scale. Field experts evaluated the scale's content validity, and feedback from 15 preservice teachers was gathered to inform necessary revisions. An exploratory factor analysis of data from 315 preservice teachers revealed a three-factor structure comprising 39 items. The scale explained 53.23% of the total variance. Confirmatory factor analysis performed on data from another group of 357 preservice teachers supported the exploratory factor analysis results. The fit indices are as follows: χ²/df = 2.86; RMSEA = .072; CFI = .96; NNFI = .95; NFI = .93; IFI = .96; RFI = .92. Convergent and discriminant validity analyses revealed that the measurement model has a generally acceptable level of validity. Furthermore, the high Cronbach's alpha coefficient indicates the scale's reliability. The final scale consists of 39 items: 28 are positively worded, and 11 are negatively worded. These findings indicate that the scale is both valid and reliable, providing a robust tool for measuring preservice teachers' attitudes toward AI technology.

Özet

This study aimed to develop a measurement tool to assess preservice teachers' attitudes toward artificial intelligence technology. In this study, a cross-sectional design was employed within the scope of quantitative research. The study sample was selected using a convenient sampling method and comprised 672 preservice teachers from various departments and grade levels in the Faculty of Education of a state university. The scale development process has been carried out systematically. A pool of 70 items was generated from the relevant literature and formatted as a Likert-type scale. Field experts evaluated the scale's content validity, and feedback from 15 preservice teachers was gathered to inform necessary revisions. An exploratory factor analysis of data from 315 preservice teachers revealed a three-factor structure comprising 39 items. The scale explained 53.23% of the total variance. Confirmatory factor analysis performed on data from another group of 357 preservice teachers supported the exploratory factor analysis results. The fit indices are as follows: χ²/df = 2.86; RMSEA = .072; CFI = .96; NNFI = .95; NFI = .93; IFI = .96; RFI = .92. Convergent and discriminant validity analyses revealed that the measurement model has a generally acceptable level of validity. Furthermore, the high Cronbach's alpha coefficient indicates the scale's reliability. The final scale consists of 39 items: 28 are positively worded, and 11 are negatively worded. These findings indicate that the scale is both valid and reliable, providing a robust tool for measuring preservice teachers' attitudes toward AI technology.

🔖

Cite This Article

Alan, B., Zengin, F. K., Keçeci, G. (2026). Developing an attitude scale for preservice teachers towards artificial intelligence technology: A validity and reliability study. International Journal of Assessment Tools in Education, Volume: 13 Issue: 3. https://doi.org/10.21449/ijate.1671049

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (87)

  1. Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması [Artificial intelligence anxiety (AIA) scale: Adaptation to Turkish, validity and reliability study]. Alanya Academic Review, 5(2), 1125 1146. https://doi.org/10.29023/alanyaakademik.833668
  2. Alan, B., Keçeci, G., & Kırbağ Zengin, F. (2025). Öğretmenler için yapay zekâ tutum ölçeği: Geçerlik ve güvenirlik çalışması [Artificial intelligence attitude scale for teachers: Validity and reliability study]. Pamukkale University Journal of Education (63), 120 149. https://doi.org/10.9779/pauefd.1418456
  3. Alan, B., Zengin, F., & Keçeci, G. (2024). Yapay zekâ tutum ölçeği (YZTÖ): Geçerlik ve güvenirlik çalışması [Artificial intelligence attitude scale (AIAS): Validity and reliability study]. Cumhuriyet International Journal of Education, 13(4), 789-800. https://doi.org/10.30703/cije.1327949
  4. Attwell, G., Bekiaridis, G., Deitmer, L., Perini, M., Roppertz, S., & Tütlys, V. (2020). Artificial intelligence in policies, processes and practices of vocational education and training. Institut Technik und Bildung (ITB). https://doi.org/10.26092/elib/307
  5. Bang, K.S., Kim, S., Kim, W., Choi, S., Jeong, Y., & Choe, J. H. (2024). Validity and reliability of the Korean version of the trauma-informed climate scale-10. Asian Nursing Research, 18(5), 460-467. https://doi.org/10.1016/j.anr.2024.10.002
  6. Batuk, B., Kaya, A., Yildirim, O., & Türk, N. (2024, May 16-19). Investigation of variables predicting university students’ acceptance levels of generative artificial intelligence [Paper presentation]. 25th International Psychological Counseling and Guidance Congress, Ankara, Türkiye.
  7. Beaulac, C., & Rosenthal, J.S. (2019). Predicting university students’ academic success and major using random forests. Research in Higher Education, 60, 1048-1064. https://doi.org/10.1007/s11162-019-09546-y
  8. Becker, S.A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). Horizon report> 2018 higher education edition. EDUCAUSE. https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf
  9. Bland, J.M., & Altman, D.G. (1997). Statistics notes: Cronbach's alpha. BMJ, 314(7080), Article 572. https://doi.org/10.1136/bmj.314.7080.572
  10. Büyüköztürk S. (2015). Sosyal bilimler için veri analizi el kitabı [Manual of data analysis for social sciences] (21th ed.). Pegem Publishing.
  11. Cameron, A. (2004). Kurtosis. In Lewis-Beck, M., Bryman, A., & Liao, T. (Eds.). The sage encyclopedia of social science research methods (pp. 544-545). SAGE Publications.
  12. Can, A. (2017). SPSS ile bilimsel araştırma sürecinde nicel veri analizi [Quantitative data analysis in the scientific research process with SPSS] (5th ed.). Pegem Publishing.
  13. Canbazoğlu Bilici, S., Tanrisevdi, M., Yıldız Durak, H., & Çakıroğlu, J. (2024, August 25-26). Yapay zekâ teknolojik pedagojik alan bilgisi (YZ-TPAB) ölçeğinin Türkçeye uyarlaması [Adaptation of artificial intelligence technological pedagogical content knowledge (AI-TPACK) scale into Turkish] [Paper presentation]. 5th International Conference on Engineering and Applied Natural Sciences, Konya, Türkiye.
  14. Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66 (4), 616 630. https://doi.org/10.1007/s11528-022-00715-y
  15. Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16 24. https://doi.org/10.1016/j.procs.2018.08.233
  16. Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28-47. https://www.jstor.org/stable/48647028
  17. Chiu, T.K., Xia, Q., Zhou, X., Chai, C.S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118
  18. Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed.). Routledge.
  19. Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291 306. https://doi.org/10.52911/itall.1401740
  20. Çokluk, O., Şekercioğlu, G., & Büyüköztürk, S. (2012). Sosyal bilimler için çok değişkenli istatistik SPSS ve LISREL uygulamaları [SPSS and LISREL applications of multivariate statistics for the social sciences]. Pegem Publishing.
  21. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  22. Derinalp, P., & Ozyurt, M. (2024). Adaptation of the student attitudes toward artificial intelligence scale to the turkish context: Validity and reliability study. International Journal of Human-Computer Interaction, 41(8), 4653-4667. https://doi.org/10.1080/10447318.2024.2352921
  23. DeVellis, R.F., & Thorpe, C.T. (2021). Scale development: Theory and applications. Sage Pablications.
  24. Dinler, H. (2025). Development of the artificial intelligence perception and attitude scale (AIPAS): Validity and reliability study. Bartın University Journal of Faculty of Education, 14(4), 1283-1304. https://doi.org/10.14686/buefad.1602673
  25. Doğan, F.N. (2025). Türkçe öğretmeni adaylarının yapay zekâya yönelik tutumları ile yapay zekâ okuryazarlık düzeyleri arasındaki ilişki [The relationship between turkish teacher candidates' attitudes towards artificial intelligence and their levels of artificial intelligence literacy]. International Journal of Language Academy, 13(56), 197-215. https://doi.org/10.29228/ijla.88194
  26. Dotter, A., Chaboyer, B., Jevremović, D., Kostov, V., Baron, E., & Ferguson, J.W. (2008). The Dartmouth stellar evolution database. The Astrophysical Journal Supplement Series, 178(1), 89-101. https://doi.org/10.1086/589654
  27. Eagly, A.H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers.
  28. Eickelmann, B., Drossel, K., & Heldt, M. (2021). ICT in teacher education and ICT-related teacher professional development in Germany. In J.C.-K. Lee & T. Ehmke (Eds.), Quality in teacher education and professional development: Chinese and German perspectives (pp. 107-124). Routledge. https://doi.org/10.4324/9781003197973-8
  29. Ferikoglu, 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
  30. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
  31. 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/10.2307/3151312
  32. Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., & Gerjets, P. (2023). ChatGPT in education: Global reactions to AI innovations. Scientific Reports, 13(1), Article 15310. https://doi.org/10.1038/s41598-023-42227-6
  33. Galindo-Domínguez, H., Sainz de la Maza, M., Campo, L., & Losada, D. (2024). Design and validation of a multidimensional scale for assessing teachers’ perceptions towards artificial intelligence in education. International Journal of Learning Technology, 126, Article 102381. https://doi.org/10.1016/j.ijer.2024.102381
  34. George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.
  35. 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
  36. Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2009). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
  37. Hajkowicz, S., Sanderson, C., Karimi, S., Bratanova, A., & Naughtin, C. (2023). Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021. Technology in Society, 74, Article 102260. https://doi.org/10.1016/j.techsoc.2023.102260
  38. Harry, A. (2023). Role of AI in education. Interdiciplinary Journal and Hummanity (INJURITY), 2(3), 260-268. https://doi.org/10.58631/injurity.v2i3.52
  39. Henseler, J., Ringle, C.M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
  40. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org
  41. Karaman, K., & Köse, M. (2025). Fen bilimleri öğretmen ve öğretmen adaylarının yapay zekaya yönelik tutumları ile yapay zeka okuryazarlık düzeyleri: İlişkisel bir inceleme [Artificial intelligence attitudes and artificial intelligence literacy levels of science teachers and pre-service science teachers: A correlational study]. Journal of New Approaches in Education, 8(2), 177 197. https://doi.org/10.70325/eyyad.1782927
  42. Karaoğlan Yılmaz, F.G., & Yılmaz, R. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçeye uyarlanması [Adaptation of artificial intelligence literacy scale into Turkish]. Bilgi ve İletişim Teknolojileri Dergisi, 5(2), 172-190. https://doi.org/10.53694/bited.1376831
  43. Karaoglan Yilmaz, F.G., Yilmaz, R., & Ceylan, M. (2023). 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
  44. Karayiğit, M.G., Çilingir Altıner, E., & Ayanoğlu, K. (2025). Sınıf öğretmeni adaylarının yapay zekâya yönelik tutumlarının belirlenmesi [Determining pre-service primary school teachers’ attitudes towards artificial intelligence]. International Primary Education Research Journal, 9(2), 289-300. https://izlik.org/JA54HG64DU
  45. Kaya, F., Aydin, F., alanman, A., Rodway, P., Yetisensoy, O., & Demir-Kaya, M. (2022). The roles of personality traits, al anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human Computer Interaction, 40(2), 497 514. https://doi.org/10.1080/10447318.2022.2151730
  46. Kline, R.B. (2016). Principle and practice of structural equation modelling (4th ed.). The Guilford Press.
  47. Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024). Will artificial intelligence drive the advancements in higher education? A tri-phased exploration. Technological Forecasting and Social Change, 201, Article 123258. https://doi.org/10.1016/j.techfore.2024.123258
  48. Kurtboğan, H., & Ak, M. (2024). İnsanlığın pi noktası: Yapay zekâ [Humanity's pi point: Artificial intelligence]. Nobel Bilimsel Eserler.
  49. Lawshe, C.H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
  50. Leech, N.L., Barrett, K.C., & Morgan, G.A. (2005). SPSS for intermediate statistics: Use and interpretation. (2th ed.). Lawrence Erlbaum Associates.
  51. Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
  52. Mart, M., & Kaya, G. (2024). Okul öncesi öğretmen adaylarının yapay zekâya yönelik tutumları ve yapay zekâ okur yazarlığı arasındaki ilişkinin incelenmesi [The examination of preschool teacher candidates’ attitudes towards artificial intelligence and their artificial intelligence literacy relationship]. Edutech Research, 2(1), 91-109.
  53. Martin, F., Mahipal, V., Jain, G., Ghosh, S., & Sanusi, I.T. (2024, March). ChemAIstry: A novel software tool for teaching model training in K-8 education. In Proceedings of the 55th ACM technical symposium on computer science education V. 1 (pp. 792-798). Association for Computing Machinery. https://doi.org/10.1145/3626252.3630804
  54. Martínez-Comesaña, M., Rigueira-Díaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J., Ocarranza-Prado, I., & Kreibel, D. (2023). Impact of artificial intelligence on assessment methods in primary and secondary education: systematic literature review. Revista de Psicodidáctica (English ed.), 28(2), 93-103. https://doi.org/10.1016/j.psicoe.2023.06.002
  55. Martínez-Moreno, J., & Petko, D. (2024). What motivates future teachers? The influence of Artificial Intelligence on student teachers' career choice. Computers and Education: Artificial Intelligence, 7, Article 100296. https://doi.org/10.1016/j.caeai.2024.100296
  56. Murphy, R.F. (2019). Artificial intelligence applications to support K-12 teachers and teaching: A review of promising applications, opportunities, and challenges. Rand Corporation, 10(1), 1-20. https://www.jstor.org/stable/resrep19907
  57. Nguyen, L.A.T., & Habók, A. (2022). Adaptation and validation of a computer-assisted language learning attitude questionnaire in a Vietnamese EFL context: A comparison between online and paper modes of administration. Heliyon, 8(6), Article e09743. https://doi.org/10.1016/j.heliyon.2022.e09743
  58. Ozudogru, G., & Yildiz Durak, H. (2024). Turkish adaptation of the artificial intelligence readiness scale for preservice teachers. In M. Coşkun Ögeyik & H.R. Ordóñez Zúñiga (Eds.), 10th International New York Academic Research Congresses on Social, Humanities, Administrative, and Educational Sciences proceedings book (pp. 400–410). BZT Turan Publishing House.
  59. Pallant, J. (2010). SPSS survival manual a step by step guide to data analysis using SPSS (4th ed.). McGraw-Hill.
  60. Polatgil, M., & Güler, A. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçe’ye uyarlanması [Adaptation of artificial intelligence literacy scale into Turkish]. Journal of Quantitative Research in Social Sciences 3(2), 99-114. https://sobinarder.com/index.php/sbd/article/view/65
  61. Popenici, S.A.D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12 (1), Article 22. https://doi.org/10.1186/s41039-017-0062-8
  62. Raykov, T. (1998). Coefficient alpha and composite reliability with interrelated nonhomogeneous items. Applied Psychological Measurement, 22(4), 375-385. https://doi.org/10.1177/014662169802200407
  63. Rokeach, M. (1969). Beliefs, attitudes, and values: A theory of organization and change. Jossey-Bass Inc., Publishers.
  64. Sanusi, I.T., & Oyelere, S.S. (2020). Pedagogies of machine learning in K-12 context. In Proceedings of the 2020 IEEE frontiers in education conference (FIE) (pp. 1 8). IEEE. http://doi.org/10.1109/FIE44824.2020.9274129
  65. Sanusi, I.T., Agbo, F.J., Dada, O.A., Yunusa, A.A., Aruleba, K.D., Obaido, G., Olawumi, O., & Oyelere, S.S. (2024). Stakeholders’ insights on artificial intelligence education: Perspectives of teachers, students, and policymakers. Computers and Education Open, 7, Article 100212. https://doi.org/10.1016/j.caeo.2024.100212
  66. Savas, B.Ç. (2024). Yapay zekâya bağımlılık ölçeğinin Türkçe’ye uyarlanması: Geçerlik ve güvenirlik çalışması [Adaptation of the dependence to artificial ıntelligence scale into Turkish: Validity and reliability study]. Journal of Sports for All and Recreation, 6(3), 306 315. https://doi.org/10.56639/jsar.1509301
  67. Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, Article 100014. https://doi.org/10.1016/j.chbr.2020.100014
  68. Schepman, A., & Rodway, P. (2023). The general attitudes towards artificial intelligence scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human Computer Interaction, 39(13), 2724 2741. https://doi.org/10.1080/10447318.2022.2085400
  69. Seçer, I. (2018). Psikolojik test geliştirme ve uyarlama süreci, SPSS ve LISREL uygulamaları [Psychological test development and adaptation process, SPSS and LISREL applications] (2nd ed.). Anı Publishing.
  70. Seldon, A., & Abidoye, O. (2018). The fourth education revolution. Legend Press Ltd.
  71. Sencan, H. (2005). Sosyal ve davranışsal ölçümlerde güvenilirlik ve geçerlilik [Reliability and validity in social and behavioral measures] (1st ed.). Seçkin Publishing.
  72. Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H.S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche Intelligenz, 35(1), 109-118. https://doi.org/10.1007/s13218-020-00689-0
  73. Süleymanoğulları, M., Özdemir, A., & Tekin, A. (2024). Yapay zekâ ölçeği: Geçerlik ve güvenirlik çalışması [Artificial intelligence scale: Validity and reliability study]. Education, Science and Sport,6(1), 13-27. https://dergipark.org.tr/en/download/article-file/4048642
  74. Şahin, N.H., Batıgün, A.D., Yasak, Y., & Eyrikaya, E. (2026). Yapay zekâya ilişkin tutumlar ölçeği: Geliştirme, geçerlik ve güvenirlik çalışması [Attitudes toward artificial intelligence scale: Development, validity, and reliability study]. Journal of Clinical Psychology Research, (Advanced Online Publication), 1-14. https://doi.org/10.57127/kpd.26024438.1786161
  75. Tabachnick B.G., & Fidell L.S. (2001). Using multivariate statistics (4th ed.). Allyon and Bacon.
  76. Tavşancıl, E. (2005). Tutumların ölçülmesi ve SPSS ile veri analizi [Measurement of attitudes and data analysis with SPSS] (2nd ed.). Nobel Publishing.
  77. Terzi, R. (2020). An adaptation of artificial intelligence anxiety scale into Turkish: Reliability and validity study. International Online Journal of Education and Teaching, 7(4), 1501 1515. http://iojet.org/index.php/IOJET/article/view/1031
  78. Uzum, B., Elcicek, M., & Pesen, A. (2024). Development of teachers’ perception scale regarding artificial intelligence use in education: Validity and reliability study. International Journal of Human-Computer Interaction, 41(5), 2776-2787. https://doi.org/10.1080/10447318.2024.2385518
  79. 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
  80. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems With Applications, 252, Article 124167. https://doi.org/10.1016/j.eswa.2024.124167
  81. Wang, Y.Y., & Wang, Y.S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 1-16. https://doi.org/10.1080/10494820.2019.1674887
  82. 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
  83. Worthington, R.L., & Whittaker, T.A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806 838. https://doi.org/10.1177/0011000006288127
  84. Xia, Q., Chiu, T.K., Lee, M., Sanusi, I.T., Dai, Y., & Chai, C.S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, Article 104582. https://doi.org/10.1016/j.compedu.2022.104582
  85. Yıldırım, G., & Yıldırım, S. (2025). Yapay zekâ araçlarının kullanımına yönelik tarih öğretmen adaylarının genel tutumları [General attitudes of pre-service history teachers towards the use of artificial intelligence tools]. Bayburt Eğitim Fakültesi Dergisi, 20(45), 243 259. https://doi.org/10.35675/befdergi.1586414
  86. Zawacki-Richter, O., Marín, V.I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1 27. https://doi.org/10.1186/s41239-019-0171-0
  87. Zhang, K., & Aslan, A.B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025