Research Article
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Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Öğretmenlerin Yapay Zeka Tutum Ölçeğinin Geliştirilmesi: Bir Geçerlilik ve Güvenirlik Çalışması

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

Bu nicel araştırmanın amacı, öğretmenlerin yapay zekâya yönelik tutumlarını belirlemeye yönelik bir ölçme aracı geliştirmektir. Araştırmanın çalışma grubunu, 385 öğretmen oluşturmaktadır. Başlangıçta 34 maddeden oluşan ölçek taslağı, bu gruba uygulanarak geçerlik ve güvenirlik analizleri gerçekleştirilmiştir. Ölçeğin Kaiser-Meyer-Olkin (KMO) değeri .970, Bartlett Küresellik Testi sonucu ise χ² = 6965.978, df = 253, p < .001 olarak bulunmuştur. Yapılan Açıklayıcı Faktör Analizi (AFA) sonucunda faktör yükü düşük olan ve binişiklik oluşturan 6 madde ile düzeltilmiş madde-toplam korelasyonu düşük olan 1 madde ölçekten çıkarılmış, böylece 23 maddelik 5’li Likert tipi bir ölçek elde edilmiştir. Kapsam geçerliliği uzman görüşleriyle sağlanan ölçek üzerinde daha sonra yapılan Doğrulayıcı Faktör Analizi (DFA) sonucunda faktör yükü düşük olan 1 maddenin daha çıkarılmasıyla, ölçeğin nihai hali 22 madde olarak belirlenmiştir. Ölçek maddeleri ile alt boyutlar arasındaki ilişkileri belirlemek amacıyla hesaplanan Pearson korelasyon katsayıları, ölçek ile alt boyutları arasında yüksek ve anlamlı ilişkiler olduğunu göstermiştir. Ayrıca, ölçeğin iç tutarlılığına ilişkin yapılan analizde Cronbach Alfa katsayısı 0,965 olarak saptanmış, bu da ölçeğin yüksek düzeyde güvenilir olduğunu ortaya koymuştur. Sonuç olarak, elde edilen bulgular doğrultusunda geliştirilen bu ölçeğin, öğretmenlerin yapay zekâya yönelik tutumlarını geçerli ve güvenilir bir biçimde ölçebilecek nitelikte olduğunu göstermiştir.

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9

Development of Teachers’ Attitude Scale Towards Artificial Intelligence: A Validity and Reliability Study

Year 2025, Volume: 9 Issue: 33, 1 - 22, 25.09.2025
https://doi.org/10.31455/asya.1712885

Abstract

The primary aim of this quantitative study is to develop a measurement tool designed to assess teachers’ attitudes toward artificial intelligence (AI). The study included 385 teachers. Initially, a draft version of the scale comprising 34 items was administered to this group, and validity and reliability analyses were conducted accordingly. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was found to be .970, and the result of Bartlett’s Test of Sphericity was χ² = 6965.978, df = 253, p < .000, indicating that the data were suitable for factor analysis. As a result of the EFA, six items with low factor loadings or cross-loadings and one item with a low corrected item-total correlation were removed, yielding a 23-item Likert-type scale. Then, a CFA was performed. Accordingly, one additional item with a low factor loading was excluded, resulting in a final version of the scale comprising 22 items. Pearson correlation indicated strong and statistically significant correlations between the sub-factors of the instrument. Furthermore, the internal consistency of the scale was supported by a Cronbach’s alpha coefficient of .965, demonstrating a high level of reliability. In conclusion, the findings suggest that the developed scale is a valid and reliable instrument for measuring teachers' attitudes toward artificial intelligence.

References

  • Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00930-x
  • Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
  • Balcı, A. (1995). Sosyal bilimlerde araştırma: Yöntem, teknik ve ilkeler. Ankara Üniversitesi Eğitim Fakültesi Yayınları.
  • Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
  • Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (23. baskı). Ankara: Pegem Akademi.
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2017). Bilimsel araştırma yöntemleri (23. baskı). Ankara: Pegem Akademi.
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). London: Routledge.
  • Chen, I. H., Chen, J. V., & Kim, Y. (2020). Can I be as cool as AI? The role of AI identity in shaping AI-related learning attitudes. Computers & Education, 157. https://doi.org/10.1016/j.compedu.2020.103970
  • Chen, J., & Lee, Y. (2022). Teachers’ concerns about AI in education: A critical review. Teaching and Teacher Education, 110. https://doi.org/10.1016/j.tate.2021.103590
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
  • Cope, B., Kalantzis, M., Searsmith, D., & Woods, A. (2021). Artificial intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(2), 117–132. https://doi.org/10.1080/00131857.2020.1835647
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9. Retrieved from https://pareonline.net/getvn.asp?v=10&n=7
  • Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2010). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları. Pegem Akademi.
  • 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
  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th ed.). SAGE Publications.
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
  • Gök, T. (2022). Öğretmenlerin yapay zekâ teknolojilerine yönelik farkındalık ve tutumları. Eğitim Teknolojisi Kuram ve Uygulama, 12(2), 184–204. Retrieved from https://dergipark.org.tr/tr/pub/etku/issue/74121/1162435
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Indianapolis: Pearson Education.
  • Haynes, S. N., Richard, D. C. S., & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 7(3), 238–247. https://doi.org/10.1037/1040-3590.7.3.238
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
  • Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Rodrigo, M. M. T. (2021). 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
  • Holmes, W., Sutherland, E., & Joseph, S. (2022). Artificial intelligence and the future of teaching and learning. UNESCO Education Sector Reports.
  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
  • Howard, S. K., Tondeur, J., Ma, J., & Yang, J. (2021). What to teach? Strategies for developing digital competency in preservice teacher training. Computers & Education, 165, 104149. https://doi.org/10.1016/j.compedu.2021.104149
  • 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
  • Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in education: A bibliometric mapping analysis over the past two decades. Interactive Learning Environments, 29(1), 1–15. https://doi.org/10.1080/10494820.2021.1952615
  • Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the sımplıs command language. Scientific Software International.
  • Karasar, N. (1999). Bilimsel araştırma yöntemi (9. baskı). Ankara: Nobel Yayın Dağıtım.
  • Karataş, K., & Öztürk, M. (2021). Development of an attitude scale toward artificial intelligence. Education and Information Technologies, 26(6), 7569–7586.
  • Kim, Y., Park, H., & Kang, M. (2021). Measuring digital competence of teachers: Development and validation of a self-assessment instrument. Computers & Education, 168, 104198. https://doi.org/10.1016/j.compedu.2021.104198
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  • Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. Retrieved from https://parsmodir.com/wp-content/uploads/2015/03/lawshe.pdf.
  • Lin, Y. L., Huang, H. J., & Chuang, Y. H. (2021). Investigating the impact of teacher professional development on technology integration. Educational Technology Research and Development, 69(2), 1031–1055. https://doi.org/10.1007/s11423-020-09884-5
  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. UNESCO.
  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Indianapolis: Pearson Education.
  • Maruyama, G. (1998). Basics of structural equation modeling. Los Angeles: Sage Publications.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York City: McGraw-Hill.
  • Renz, A., Krishnaraja, S., & Gronau, N. (2021). Artificial intelligence in education: Challenges and opportunities for sustainable development. Sustainability, 13(2), 652. https://doi.org/10.3390/su13020652
  • Sahin, I., & Kilic, M. (2023). Developing a scale to measure teachers’ attitudes towards artificial intelligence technologies. Educational Technology & Society, 26(1), 145–160. Retrieved from https://dergipark.org.tr/en/pub/etku/issue/74121/1162435
  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Cambridge: Polity Press.
  • Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston: Allyn and Bacon.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Indianapolis: Pearson Education.
  • Tavşancıl, E. (2010). Tutumların ölçülmesi ve SPSS ile veri analizi (4. baskı). Ankara: Nobel Yayın Dağıtım.
  • Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
  • Tezbaşaran, A. A. (1997). Likert tipi ölçek geliştirme kılavuzu. Ankara: Türk Psikologlar Derneği Yayınları.
  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
  • 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
  • Yurdugül, H. (2005). Ölçek geliştirme çalışmalarında kapsam geçerliği için kapsam geçerlik indekslerinin kullanılması. XIV. Ulusal Eğitim Bilimleri Kongresi Bildirileri, Pamukkale Üniversitesi, Denizli.
  • Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
  • Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Center for the Governance of AI, Future of Humanity Institute, University of Oxford. http://dx.doi.org/10.2139/ssrn.3312874
  • Zhang, H., & Aslan, A. (2021). Teachers’ professional development in artificial intelligence: An integrative review. Education and Information Technologies, 26, 625–652. Retrieved from https://link.springer.com/article/10.1007/s10639-025-13478-9
There are 57 citations in total.

Details

Primary Language Turkish
Subjects Scale Development
Journal Section Articles
Authors

Seyithan Demirdağ 0000-0002-4083-2704

İsa Kürşad Ünver 0000-0001-9707-6527

Işıl Gürez 0000-0001-7883-8098

Publication Date September 25, 2025
Submission Date June 3, 2025
Acceptance Date August 15, 2025
Published in Issue Year 2025 Volume: 9 Issue: 33

Cite

APA Demirdağ, S., Ünver, İ. K., & Gürez, I. (2025). Öğretmenlerin Yapay Zeka Tutum Ölçeğinin Geliştirilmesi: Bir Geçerlilik ve Güvenirlik Çalışması. Asya Studies, 9(33), 1-22. https://doi.org/10.31455/asya.1712885

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