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Artificial Intelligence- Supported Classroom Management Skills Scale

Yapay Zeka Destekli Sınıf Yönetimi Becerileri Ölçeği

Ercan Yilmaz(NECMETTİN ERBAKAN ÜNİVERSİTESİ)0000-0003-4702-1688
Burak Güner(NECMETTİN ERBAKAN ÜNİVERSİTESİ)0000-0002-1734-987X
Journal
SEBED
Pages
1–28
Publication Date
2026-08-06
DOI
—
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AI Summary & Key Takeaways

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

As the subscales of the scale cover the competencies required for the management of AI-supported and digital classroom environments, based on a literature review, the dimensions of the scale were named “AI integration, digital interaction, and technopedagogical infrastructure.” The reliability of the scale was tested by calculating Mc'Donals Omega, Cronbach Alpha, and Ave, and it was found that both the sub-dimensions and the total of the scale have a reliable structure.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aimed to develop a valid and reliable measurement tool capable of assessing artificial intelligence-supported classroom management skills.
📊
Key Findings & Data:During the development phase of the scale, data was collected from 239 students for exploratory factor analysis and 247 students for confirmatory factor analysis.
💡
Conclusion & Impact:As the subscales of the scale cover the competencies required for the management of AI-supported and digital classroom environments, based on a literature review, the dimensions of the scale were named “AI integration, digital interaction, and technopedagogical infrastructure.” The reliability of the scale was tested by calculating Mc'Donals Omega, Cronbach Alpha, and Ave, and it was found that both the sub-dimensions and the total of the scale have a reliable structure.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · Dijital Sınıf · Dijital Sını Becerileri

Abstract

This study aimed to develop a valid and reliable measurement tool capable of assessing artificial intelligence-supported classroom management skills. The artificial intelligence-supported classroom management skills scale was designed based on the literature on digital classroom management and artificial intelligence-supported classroom management. During the development phase of the scale, data was collected from 239 students for exploratory factor analysis and 247 students for confirmatory factor analysis. Data was also collected from a total of 78 students during other phases, such as the pilot application, item comprehensibility, and determining the duration of the measurement tool. The construct validity of the scale was first tested with EFA and then with CFA. The analyses revealed that the scale consists of a total of 22 items and three subscales. As the subscales of the scale cover the competencies required for the management of AI-supported and digital classroom environments, based on a literature review, the dimensions of the scale were named “AI integration, digital interaction, and technopedagogical infrastructure.” The reliability of the scale was tested by calculating Mc'Donals Omega, Cronbach Alpha, and Ave, and it was found that both the sub-dimensions and the total of the scale have a reliable structure. The analyses conducted within the scope of the study showed that the scale is a highly valid and reliable measurement tool that can measure the specified quality.

Özet

Bu araştırma kapsamında yapay zeka destekli sınıf yönetimi becerilerini ölçebilen geçerli ve güvenilir bir ölçme aracı geliştirme amaçlanmıştır. Yapay zeka destekli sınıf yönetimi becerileri ölçeği, dijital sınıf yönetimi, yapay zeka destekli sınıf yönetimi literatürüne göre tasarlanmıştır. Ölçeğin geliştirilme aşamasında açımlayıcı faktör analizi için 239 öğrenci, doğrulayıcı faktör analizi için ise 247 öğrenci, pilot uygulama, maddelerin anlaşılırlığı, ölçme aracının süresinin belirlenmesi gibi diğer aşamalarda ise toplamda 78 öğrenciden veri toplanmıştır. Ölçeğin yapı geçerliği ilk olarak AFA ve sonrasında ise DFA ile sınanmıştır. Analizler sonucunda ölçeğin toplamda 22 maddeden ve üç alt boyuttan oluşmuştur. Ölçeğin alt boyutları yapılan literatür taraması sonucunda yapay zeka destekli ve dijital sınıf ortamlarının yönetiminde bulunması gereken yeterlikleri kapsadığından, ölçeğin boyutları “yapay zeka entegrasyonu, dijital etkileşim ve teknopedagojik altyapı” olarak isimlendirilmiştir. Mc’Donals Omega, Cronbach Alpha ve Ave hesaplanarak ölçeğin güvenirliği test edilmiş ve ölçeğin hem alt boyutlarının hem de toplamının güvenilir bir yapıya sahip olduğu görülmüştür. Çalışma kapsamında yapılan analizler ölçeğin belirlenen niteliği ölçebilen, yüksek düzeyde geçerli ve güvenilir bir ölçme aracı olduğunu göstermiştir.

🔖

Cite This Article

Yilmaz, E., Güner, B. (2026). Artificial Intelligence- Supported Classroom Management Skills Scale. SEBED, Sayı: 1, 1–28. https://ajindex.com/articles/1646378

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References (108)

  1. Ada, S. & Baysal, Z.N. (2020). Sınıf yönetimi (2. Baskı). Nobel.
  2. Ali, Q., Abbas, A., Raza, A., Khan, M. T. I., Zulfiqar, H., Iqbal, M. A., … & Alotaibi, B. A. (2023). Exploring the students’ perceived effectiveness of online education during the covid-19 pandemic: Empirical analysis using structural equation modeling (SEM). Behavioral Sciences, 13(7), 578. https://doi.org/10.3390/bs13070578
  3. Almuqhim, S. & Berri, J. (2025). AI‐driven personalized microlearning framework for enhanced e‐learning. Computer Applications in Engineering Education, 33(3). https://doi.org/10.1002/cae.70040
  4. Al-Sindi, T., Putra, H. D., & Ghozi, S. (2023). Integrating technology into classroom training. Journal of Training, Education, Science and Technology, 1-6. https://doi.org/10.51629/jtest.v1i1.168
  5. Ananda, K. R., Tjalla, M., Fakhruddin, Z., Mujahidah, M., & Sunubi, A. H. (2025). WEB 2.0 tools in English project based learning at elementary and high schools in Kabupaten Pinrang. DEIKTIS: Jurnal Pendidikan Bahasa Dan Sastra, 5(1), 81-88. https://doi.org/10.53769/deiktis.v5i1.1295
  6. Andhika, A., Aldila, A. S., Supriyono, L. A., Previana, C. N., & Habibie, D. R. (2024). The effectiveness of adaptive learning systems ıntegrated with LMS in higher education. Jurnal KomtekInfo, 49-56. https://doi.org/10.35134/komtekinfo.v11i2.505
  7. Atun, H. & Usta, E. (2019). The effects of programming education planned with TPACK framework on learning outcomes. Participatory Educational Research, 6(2), 26-36. https://doi.org/10.17275/per.19.10.6.2
  8. Avedzi, J. E. (2025). Educational technology, teaching practices, and learning outcomes in Sub-Saharan Africa: Empirical insights from Ghana’s basic education classrooms. https://doi.org/10.21203/rs.3.rs-7330474/v1
  9. Ayanwale, M. A., Adelana, O. P., & Odufuwa, T. T. (2024). Exploring STEAM teachers’ trust in AI-based educational technologies: a structural equation modelling approach. Discover Education, 3(1). https://doi.org/10.1007/s44217-024-00092-z
  10. Badawy, W., Zinhom, H., & Shaban, M. (2024). Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review. International Nursing Review, 72(3). https://doi.org/10.1111/inr.13059
  11. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. MIT Press. https://fairmlbook.org
  12. Begum, I. U. (2024). Role of artificial ıntelligence in higher education- An empirical investigation. International Research Journal on Advanced Engineering and Management (IRJAEM), 2(03), 49-53. https://doi.org/10.47392/irjaem.2024.0009
  13. Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 1–24. https://doi.org/10.1186/s41239-021-00282-x
  14. Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York: The Guilford Press.
  15. Browne, M.W. and Cudeck, R. (1992) Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230-258. https://doi.org/10.1177/0049124192021002005
  16. Büyüköztürk, Ş. (2024). Sosyal Bilimler İçin Veri Analizi El Kitabı (31. Baskı). Pegem Akademi.
  17. Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Lawrence Erlbaum Associates Publishers.
  18. Byrne, B. M. (2010). Structural Equation Modeling with AMOS. Routledge.
  19. Calvani, A., Fini, A., Ranieri, M., & Picci, P. (2012). Are young generations in secondary school digitally competent? A study on Italian teenagers. Computers & Education, 58(2), 797-807.
  20. Can, E. & Gündüz, Y. (2021). Öğretmenlerin sanal sınıf yönetimi yeterliği ölçeği: Geçerlik ve güvenirlik çalışması. Bolu Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 21(2), 574-591. https://dx.doi.org/10.17240/aibuefd.2021.21.62826-869543
  21. Carolus, A., Augustin, Y., Markus, A., & Wienrich, C. (2023). Digital interaction literacy model–conceptualizing competencies for literate interactions with voice-based AI systems. Computers and Education: Artificial Intelligence, 4, 100114.
  22. Chatti, M. A., Muslim, A., Guliani, M., & Guesmi, M. (2020). The LAVA model: Learning analytics meets visual analytics. In Adoption of data analytics in higher education learning and teaching (pp. 71-93). Cham: Springer International Publishing.
  23. Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment Research & Evaluation, 10, 1-9.
  24. Çelik, İ. (2022). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behaviour, 138. https://doi.org/10.1016/j.chb.2022.107468
  25. Dampson, D. G. (2021). Determinants of learning management system adoption in an era of covıd-19: Evidence from a Ghanaian University. European Journal of Education and Pedagogy, 2(3), 80-87. https://doi.org/10.24018/ejedu.2021.2.3.94
  26. Davids, A. I. R., Camarero‐Figuerola, M., & Camacho, M. d. M. (2025). Navigating the challenges and opportunities of artificial intelligence in educational leadership: A scoping review. Review of Education, 13(2). https://doi.org/10.1002/rev3.70101
  27. DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Thousand Oaks, CA: Sage.
  28. Dignum, V. (2019). Responsible Artificial Intelligence: Developing and Using AI in a Responsible Way. Springer. https://doi.org/10.1007/978-3-030-30371-6
  29. Dziuban, C. D., Graham, C. R., Moskal, P., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1). https://doi.org/10.1186/s41239-017-0087-5
  30. Echtenbruck, M. M., Fühles-Ubach, S., Naujoks, B., & Kaliva, E. (2025). A Data Literacy Competence Model for Higher Education and Research. arXiv preprint arXiv:2504.15690.
  31. Erdoğan, İ. (2019). Sınıf yönetimi- Ders, konferans, panel ve seminer etkinliklerinde başarının yolları (21. Baskı). Alfa.
  32. Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.
  33. Field, A.P. (2018) Discovering Statistics Using IBM SPSS Statistics. (5th Edt), Sage, Newbury Park.
  34. Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review. https://doi.org/10.1162/99608f92.8cd550d1
  35. Gharbaoui, H., Mansouri, K., & Poirier, F. (2023). Towards a social learning analysis using learning management system and learning experience to predict learners’ success. Lecture Notes in Computer Science, 364-370. https://doi.org/10.1007/978-3-031-32883-1_33
  36. Gómez, M. J. M., Sánchez, Á. A., Albaladejo‐González, M., Clemente, F. J. G., & Ruipérez‐Valiente, J. A. (2025). Utilising explainable AI to enhance real‐time student performance prediction in educational serious games. Expert Systems, 42(3). https://doi.org/10.1111/exsy.70008
  37. Grand View Research (2024). Education technology market report. https://www.grandviewresearch.com/press-release/global-education-technology-market
  38. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson
  39. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., Ray, S. (2021). An introduction to structural equation modeling. In: partial least squares structural equation modeling (pls-sem) using r. classroom companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-030-80519-7_1
  40. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
  41. Henríquez, V. & Hilliger, I. (2024). Blended learning in rural K‐12 education: Stakeholder dynamics and recommendations. Journal of Computer Assisted Learning, 40(4), 1463-1480. https://doi.org/10.1111/jcal.12963
  42. Holstein, K., McLaren, B., & Aleven, V. (2019). Designing for complementarity: Teacher and AI roles to support student learning. International Journal of Artificial Intelligence in Education, 29(3), 461–497. https://doi.org/10.1007/s40593-019-00187-8
  43. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185. https://doi.org/10.1007/BF02289447
  44. 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, 1-55. http://dx.doi.org/10.1080/10705519909540118
  45. Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: a systematic review. Educational Technology Research and Development, 68(4), 1961-1990.
  46. https://link.springer.com/article/10.1007/s11423-020-09788-z
  47. Ifenthaler, D., Mah, D. K., & Yau, J. Y. K. (2019). Utilising learning analytics for study success: Reflections on current empirical findings. In Utilizing learning analytics to support study success (pp. 27-36). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-319-64792-0_2
  48. Jian, M. J. K. O. (2023). Personalized learning through AI. Advances in Engineering Innovation, 5(1), 16-19. https://doi.org/10.54254/2977-3903/5/2023039
  49. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
  50. Kaiser, H.F. (1974) An index of factorial simplicity. Psychometrika, 39, 31-36. http://dx.doi.org/10.1007/BF02291575
  51. Keshavarz, M. & Yuan, L. Y. (2023). A narrative case history of distance education before, during, and after covid-19 in China and Iran. Canadian Journal of Learning and Technology, 49(1), 1-15. https://doi.org/10.21432/cjlt28275
  52. Kline, P. (2014). An Easy Guide to Factor Analysis. Routledge. https://doi.org/10.4324/9781315788135
  53. Krishnan, R., Nair, S., Saamuel, B. S., Justin, S., Iwendi, C., Biamba, C., … & Ibeke, E. (2022). Smart analysis of learners performance using learning analytics for ımproving academic progression: A case study model. Sustainability, 14(6), 3378. https://doi.org/10.3390/su14063378
  54. Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Liu, J. (2020). Projecting the potential impact of COVID-19 school closures on academic achievement. Educational Researcher, 49(8), 549-565.
  55. Kwangmuang, P., Jarutkamolpong, S., Sangboonraung, W., & Daungtod, S. (2021). The development of learning innovation to enhance higher order thinking skills for students in Thailand junior high schools. Heliyon, 7(6).
  56. Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “Scale for the assessment of non-experts’ AI literacy”–An exploratory factor analysis. Computers in Human Behavior Reports, 12, 100338.
  57. Li, H. (2025). Research on student learning behavior analysis and precision teaching driven by big data. Journal of Modern Educational Theory and Practice, 1(3). https://doi.org/10.70767/jmetp.v1i3.454
  58. Li, W. (2024). The influence of artificial intelligence on the education system. Lecture Notes in Education Psychology and Public Media, 65(1), 137-142. https://doi.org/10.54254/2753-7048/65/20240158
  59. Lin, C. C., Huang, A. Y., & Lu, O. H. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: a systematic review. Smart learning environments, 10(1), 41. https://link.springer.com/article/10.1186/s40561-023-00260-y
  60. Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review (Online).
  61. López-Meneses, E., López-Catalán, L., Pelícano-Piris, N. & Mellado-Moreno, P. C. (2025). Artificial Intelligence in Educational Data Mining and Human-in-the-Loop Machine Learning and Machine Teaching. Applied Sciences,15(2). https://doi.org/10.3390/app15020772
  62. Luckin, R. (2018). Machine learning and human intelligence. The future of education for the 21st century. UCL institute of education press.
  63. Mandinach, E. B., & Gummer, E. S. (2016). Data literacy for educators: Making it count in teacher preparation and practice. Teachers College Record, 118(11).
  64. Markets and Markets. (2024). AI in education market by offering (software, services), technology (machine learning, natural language processing), application (learning platforms, virtual facilitators, intelligent tutoring), end user and region - Global forecast to 2030. https://www.marketsandmarkets.com/Market-Reports/ai-in-education-market-200371366.html
  65. Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning Journal, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092
  66. Meylani, R. (2024). Artificial intelligence in the education of teachers: A qualitative synthesis of the cutting-edge research literature. Journal of Computer and Education Research, 12(24), 600-637. https://doi.org/10.18009/jcer.1477709
  67. Milli Eğitim Bakanlığı (MEB) – YEGİTEK (2025). Eğitimde Yapay Zekâ Politika Belgesi ve Eylem Planı (2025-2029). Belge, yapay zekâ sistemlerinin “bireyselleştirilmiş yönlendirme, anlık geri bildirim, öğrenme takibi” fonksiyonlarını desteklediğini resmi politika düzeyinde ifade ediyor. www.yegitek.meb.gov.tr
  68. Mozelius, P. & Hettiarachchi, E. (2017). Critical factors for implementing blended learning in higher education. International Journal of Information and Communication Technologies in Education, 6(2), 37-51. https://doi.org/10.1515/ijicte-2017-0010
  69. Mwalumbwe, I. & Mtebe, J. S. (2017). Using learning analytics to predict students’ performance in moodle learning management system: A case of mbeya university of science and technology. The Electronic Journal of Information Systems in Developing Countries, 79(1), 1-13. https://doi.org/10.1002/j.1681-4835.2017.tb00577.x
  70. Nasir, M., Hasan, M., Adlim, A., & Syukri, M. (2024). Utilizing artificial intelligence in education to enhance teaching effectiveness. Proceedings of International Conference on Education, 2(1), 280-285. https://doi.org/10.32672/pice.v2i1.1367
  71. Ndalu, U. J. (2025). Educational Management and Artificial Intelligence for Sustainable Development in Nigeria. International Journal of Educational Management, Rivers State University, 1(2), 75-89. https://ijedm.com/index.php/ijedm/article/view/46
  72. Ng, D. T. K., Leung, J. K. L., Su, J., Ng, C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137-161. https://doi.org/10.1007/s11423-023-10203-6
  73. Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082-1104.
  74. Nunnally, J.C. and Bernstein, I.H. (1994) The assessment of reliability. Psychometric Theory, 3, 248-292.
  75. OECD. (2021). OECD Digital Education Outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing. https://doi.org/10.1787/589b283f-en
  76. Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Journal of educational technology & society, 17(4), 49-64.
  77. Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: an initial investigation. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1-7. https://doi.org/10.1145/3491101.3519866
  78. Ragolane, M., Patel, S., & Salikram, P. (2024). AI versus human graders: Assessing the role of large language models in higher education. Asian Journal of Education and Social Studies, 50(10), 244-263. https://doi.org/10.9734/ajess/2024/v50i101616
  79. Rodríguez-Ortiz, M. Á., et al. (2025). Machine Learning and Generative AI in Learning Analytics. Applied Sciences, 15(15). https://doi.org/10.3390/app15158679
  80. Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley interdisciplinary reviews: Data mining and knowledge discovery, 10(3), e1355.
  81. Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2023). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. ArXiv. https://arxiv.org/pdf/2312.09548
  82. Savov, T., Terzieva, V., Todorova, K., & Kademova-Katzarova, P. (2017). Contemporary technology support for education. CBU International Conference Proceedings, 5, 802-806. https://doi.org/10.12955/cbup.v5.1029
  83. Schildkamp, K., & Poortman, C. L. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(4).
  84. Schreiber J. B. (2021). Issues and recommendations for exploratory factor analysis and principal component analysis. Research İn Social & Administrative Pharmacy: RSAP, 17(5), 1004–1011. https://doi.org/10.1016/j.sapharm.2020.07.027
  85. Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2025). The scale of artificial intelligence literacy for all (SAIL4ALL): assessing knowledge of artificial intelligence in all adult populations. Humanities and Social Sciences Communications, 12(1), 1-14.
  86. Steiger, J. H. (2007). Understanding the Limitations of Global Fit Assessment in Structural Equation Modeling. Personality and Individual Differences, 42, 893-898. https://doi.org/10.1016/j.paid.2006.09.017
  87. Sun, J. C.-Y., Lin, C.-T., & Chou, C. (2022). Teacher–student interaction and its impact on students’ engagement in online learning environments. Frontiers in Psychology, 13, 779217. https://doi.org/10.3389/fpsyg.2022.779217
  88. Szyszka, M., Tomczyk, Ł., & Kochanowicz, A. M. (2022). Digitalisation of schools from the perspective of teachers’ opinions and experiences: the frequency of ICT Use in education, attitudes towards new media, and support from management. Sustainability, 14(14), 8339. https://doi.org/10.3390/su14148339
  89. Tabachnick, B. G. & Fidell, L. S. (2013). Using multivariate statistics (Sixth Edition). USA: Pearson Education Limited.
  90. Taşdan, M. & Yurttaş, A. (2022). Sınıf yönetimi ve sanal eğitimle ilgili temel kavramlar. M. Taşdan (Ed.), Dijital çağda sınıf yönetimi içinde (ss. 3- 28). Pegem Akademi.
  91. Tondeur, J., Forkosh-Baruch, A., Prestridge, S., Albion, P., & Edirisinghe, S. (2017). Responding to challenges in teacher professional development for ICT integration in education. Educational Technology & Society, 20(1), 110–120. https://www.jstor.org/stable/90002177
  92. Topali, P., Ortega-Arranz, A., Rodríguez-Triana, M. J., Er, E., Khalil, M. & Akçapınar, G. (2024). Designing human-centered learning analytics and artificial intelligence in education solutions: a systematic literature review. Behaviour & Information Technology. https://doi.org/10.1080/0144929X.2024.2345295
  93. Turan, S. (2020). Sınıf yönetiminin temelleri. M. Şişman & S. Turan (Ed)., Sınıf yönetimi içinde (ss. 2-17). Pegem Akademi.
  94. UNESCO. (2021). Global education monitoring report 2021/2: Non-state actors in education: Who chooses? Who loses?. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54676/XJFS2343
  95. UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural Organization
  96. UNESCO. (2024). AI competency framework for teachers. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/ZJTE2084
  97. Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., … & Kizilcec, R. F. (2024). What explains teachers’ trust in AI in education across six countries?. International Journal of Artificial Intelligence in Education, 35(3), 1288-1316. https://doi.org/10.1007/s40593-024-00433-x
  98. 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 & İnformation Technology, 42(9), 1324-1337.
  99. 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, 124167. https://www.sciencedirect.com/science/article/pii/S0957417424010339#bi005
  100. Wang, T., Manta, O., & Zhang, Y. (2023). The relationship between learning motivation and online learning performance: The mediating role of academic self-efficacy and flow experience. International Journal of Emerging Technologies in Learning (iJET), 18(23), 27-38. https://doi.org/10.3991/ijet.v18i23.43977
  101. Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers İn Human Behavior, 146, 107798.
  102. Wang, Y., Wu, J., Chen, F., & Li, J. (2024). Analyzing teaching effects of blended learning with LMS: An empirical investigation. IEEE Access, 12, 42343-42356. https://doi.org/10.1109/access.2024.3352169
  103. Williamson, B., & Eynon, R. (2020). Algorithmic education governance: Regulating the digital datafication of education. Learning, Media and Technology, 45(1), 3–17. https://doi.org/10.1080/17439884.2019.1623250
  104. Woodruff, K., Hutson, J., & Arnone, K. (2023). Perceptions and barriers to adopting artificial intelligence in k-12 education: A survey of educators in fifty states. Reimagining Education - The Role of E-Learning, Creativity, and Technology in the Post-Pandemic Era. https://doi.org/10.5772/intechopen.1002741
  105. Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34, 806-838. https://doi.org/10.1177/0011000006288127
  106. Yıldırım, A., & Şimşek, H. (2018). Sosyal bilimlerde nitel araştırma yöntemleri (11. Baskı). Seçkin.
  107. Zhan, C. and Cui, P. (2023). Predicament and strategy of campus football teaching under the background of artificial intelligence and deep learning. Journal of Computational Methods in Sciences and Engineering, 23(5), 2437-2449. https://doi.org/10.3233/jcm-226840
  108. Zhou, B. (2024). The comprehensive investigation of the role related to artificial intelligence in education. Applied and Computational Engineering, 36(1), 215-219. https://doi.org/10.54254/2755-2721/36/20230449