Öğretmen Adaylarının Dijital Yeterlikleri ile Yapay Zekâ Okuryazarlıkları Arasındaki İlişkinin İncelenmesi

Öz

Bu çalışmanın amacı, öğretmen adaylarının dijital yeterlikleri ile YZ okuryazarlıkları arasındaki ilişkiyi incelemektir. İlişkisel modele göre tasarlanan çalışmanın evrenini Karadeniz Bölgesi’nde bulunan bir üniversitenin Eğitim Fakültesi öğrencileri oluşturmaktadır. Örneklemde, küme örnekleme yöntemiyle belirlenen 714 öğretmen adayı yer almaktadır. Veri toplama aracında; Kişisel Bilgi Formu, Üniversite Öğrencileri Dijital Yeterlikler Ölçeği ve Yapay Zekâ Okuryazarlığı Ölçeği bulunmaktadır. Veri analizinde bağımsız örneklemler için t- testi, tek yönlü varyans analizi, Pearson korelasyon katsayısı ve çoklu doğrusal regresyon analizi kullanılmıştır. Elde edilen bulgulara göre öğretmen adaylarının dijital yeterlikleri ile YZ okuryazarlığına yönelik algıları arasında pozitif, orta düzeyde anlamlı ilişkiler olduğu belirlenmiştir. Dijital yeterlik alt boyutlarından bilgi ve veri okuryazarlığı ile iletişim, YZ okuryazarlığını yordayıcı değişkenlerdir. Cinsiyete göre erkek öğretmen adaylarının YZ farkındalığı, kullanımı ve değerlendirmesi daha yüksek çıkmıştır. Sınıf düzeyine göre YZ okuryazarlığı farklılaşmamaktadır. Bölüme göre yapılan karşılaştırmalarda Yabancı Diller Eğitimi Bölümü öğrencilerinin puanları YZ farkındalık, kullanım ve değerlendirme boyutlarında diğer öğrencilere göre anlamlı biçimde yüksektir. Bununla birlikte gerek cinsiyet gerek bölüm değişkenlerinde ulaşılan farklılıkların etki büyüklükleri küçüktür. Çalışmanın sonuçları, öğretmen adaylarının YZ okuryazarlıklarının geliştirilmesinde dijital yeterliklerin bilgi ve veri okuryazarlığı boyutunun kritik bir bileşen olarak ele alınması gerektiğine işaret etmektedir.

Anahtar Kelimeler

• Dijital yeterlik
• Yapay zekâ
• Yapay zekâ okuryazarlığı
• Öğretmen adayları

Exploring the Relationship Between Pre-service Teachers’ Digital Competence and Artificial Intelligence Literacy

Öz

This study aims to examine the relationship between the digital competencies (DC) and artificial intelligence (AI) literacy of pre-service teachers. The study was designed as a correlational study. The study population consists of students from the Faculty of Education at a university in the Black Sea Region. The sample consists of 714 pre-service teachers selected using cluster sampling. The data collection tools used were the Personal Information Form, the University Students' Basic Digital Competences Scale, and the Artificial Intelligence Literacy Scale. The independent groups t-test, one-way analysis of variance, Pearson's moment correlation coefficient, and multiple linear regression analysis were used in data analysis. According to the study's findings, a positive, moderately significant correlation was found between pre-service teachers' DC and their perceptions of AI literacy. Information, data literacy, and communication are predictor variables of AI literacy. Male pre-service teachers showed higher levels of AI awareness, use, and evaluation. There was no difference in AI literacy across class levels. In comparisons across departments, students in the Foreign Languages Education Department showed significantly higher levels of AI awareness, use, and evaluation than students in other departments. However, the effect sizes of the differences found in both gender and department variables were small. The results indicate that the information and data literacy dimension of digital competencies should be considered a critical component in improving pre-service teachers' AI literacy.

Anahtar Kelimeler

• Digital competence
• Artificial intelligence
• Artificial intelligence literacy
• Pre-service teachers

Kaynakça

1. Afacan Adanır, G. ve Gülbahar, Y. (2022). Üniversite öğrencileri Dijital Yeterlikler Ölçeğinin Türkçeye uyarlama çalışması. Journal of Higher Education & Science/Yüksekögretim ve Bilim Dergisi, 12(1), 122-132. https://doi.org/10.5961/higheredusci.990452
2. Akbay, B., Karakoç Topal Ö., & Bostan Sarıoğlan A. (2025). Artificial intelligence literacy and anxiety levels of pre-service science teachers: A mixed method study. Journal on Efficiency and Responsibility in Education and Science, 18(4), 318–333. http://dx.doi.org/10.7160/eriesj.2025.180407
3. Aksoy, M., Aksoy, D. A., Polat, E., Hopcan, S., & Bush, A. (2026). Cross‐cultural patterns in artificial intelligence literacy development: Investigating country and gender differences among pre‐service teachers in Germany and Türkiye. European Journal of Education, 61(1), e70454. https://doi.org/10.1111/ejed.70454
4. Andreasen, J. K., Tømte, C. E., Bergan, I., & Kovac, V. B. (2022). Professional digital competence in initial teacher education: An examination of differences in two cohorts of pre-service teachers. Nordic Journal of Digital Literacy, (1), 61-74. https://doi.org/10.18261/njdl.17.1.5
5. Ata, R. & Yıldırım, K. (2019). Exploring Turkish pre-service teachers’ perceptions and views of digital literacy. Education Sciences, 9(1), 40. https://doi.org/10.3390/educsci9010040
6. Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179
7. Ayçiçek, B. (2025). Attitudes toward artificial intelligence in higher education: A mixed-methods study. Journal of University Research, 8(4), 517-534. https://doi.org/10.32329/uad.1770973
8. Ayduğ, D., & Altınpulluk, H. (2025). Are Turkish pre-service teachers worried about AI? A study on AI anxiety and digital literacy. AI & Society, 40, 5823–5834. https://doi.org/10.1007/s00146-025-02348-0

9. Bae, H., Jaesung, H., Park, J., Choi, G.W., & Moon, J. (2024). Pre-service teachers’ dual perspectives on generative AI: Benefits, challenges, and integration into their teaching and learning. Online Learning, 28(3), 131-156. https://doi.org/10.24059/olj.v28i3.4543
10. Banaz, E. ve Demirel, O. (2024). Türkçe öğretmen adaylarının yapay zekâ okuryazarlıklarının farklı değişkenlere göre incelenmesi. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi, 60, 1516-1529. https://doi.org/10.53444/deubefd.1461048
11. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-46. https://doi:10.1037/0033-2909.107.2.238
12. Bentler, P. M., & Bonnet, D. C. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606.
13. Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
14. Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students' AI self-efficacy. Computers and Education: Artificial Intelligence, 8, 100340. https://doi.org/10.13140/RG.2.2.31045.87521
15. Borisov, B., & Stoyanova, T. (2024). Artificial intelligence in higher education: Pros and cons. SCIENCE International journal, 3(2), 1-7. https://doi.org/10.35120/sciencej0302001b
16. Brown, R., Sillence, E., & Branley-Bell, D. (2025). AcademAI: Investigating AI usage, attitudes, and literacy in higher education and research. Journal of Educational Technology Systems, 54(1), 6-33. https://doi.org/10.1177/00472395251347304
17. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2012). Bilimsel araştırma yöntemleri (14. Baskı). Pegem Akademi.
18. Cetin, O. & Celen, G. (2025). Investigation of pre-service teachers’ artificial intelligence literacy and views on the use of artificial intelligence in education. Journal of Education in Science, Environment and Health (JESEH), 11(4), 285-304. https://doi.org/10.55549/jeseh.850
19. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278, https://doi.org/10.1109/ACCESS.2020.2988510
20. Chiu, T. K. F. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203. https://doi.org/10.1080/10494820.2023.2253861
21. Cohen, J. (1988). Statistical power analysis fort he behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
22. Çebi, A., & Reisoğlu, İ. (2020). Digital competence: A study from the perspective of pre-service teachers in Turkey. Journal of New Approaches in Educational Research, 9(2), 294-308. https://doi.org/10.7821/naer.2020.7.583
23. Çetin, M., & Aktaş, A. (2021). Yapay zekâ ve eğitimde gelecek senaryoları. OPUS International Journal of Society Researches, 18(Eğitim Bilimleri Özel Sayısı), 4225-4268. https://doi.org/10.26466/opus.911444
24. Deneme Gençoğlu, S., & Nalbant, S. (2025). Investigating AI literacy of Turkish pre-service EFL teachers. Journal of Education, Theory and Practical Research, 11(3), 299-313. https://izlik.org/JA92LF88NF
25. Dringó-Horváth, I., Rajki, Z., & T. Nagy, J. (2025). University teachers’ digital competence and AI literacy: Moderating role of gender, age, experience, and discipline. Education Sciences, 15(7), 868. https://doi.org/10.3390/educsci15070868
26. Dumagay, A. H. (2025). Preservice teachers and AI in education 5.0: Examining literacy, anxiety, and attitudes across gender, socioeconomic status, and training. EthAIca, 4(432), 1-15. https://doi.org/10.56294/ai2025432
27. Eriçok, B., Karataş, F., ve Yüce, E. (2024). Öğretmen adaylarının yapay zekaya (YZ) ilişkin metafor algıları. Kocaeli Üniversitesi Eğitim Dergisi, 7(2), 607-630. https://doi.org/10.33400/kuje.1511500
28. Falloon, G. (2020). From digital literacy to digital competence: The teacher digital competency (TDC) framework. Educational technology research and development, 68(5), 2449-2472. https://doi.org/10.1007/s11423-020-09767-4
29. Ferrari, A. (2012). Digital competence in practice: An analysis of frameworks (Rapor No. EUR 25351 EN). Publications Office of the European Union. https://doi.org/10.2791/82116
30. Ferrari, A., & Punie, Y. (2013). DIGCOMP: A framework for developing and understanding digital competence in Europe (Rapor No. EUR 26035 EN). European Commission, Joint Research Centre. https://doi.org/10.2788/52966
31. Ferrari, A., Punie, Y., & Redecker, C. (2012). Understanding digital competence in the 21st century: An analysis of current frameworks. In A. Ravenscroft, S. Lindstaedt, C. Delgado Kloos & D. Hernández-Leo (Ed.), 21st century learning for 21st century skills: 7th European conference on technology enhanced learning, EC-TEL 2012, Saarbrücken, Germany, September 18–21, 2012, Proceedings (Cilt 7563, ss. 79–93). Springer. https://doi.org/10.1007/978-3-642-33263-0_7
32. Field, A. (2005). Discovering statistics using SPSS (2nd edition). Sage.
33. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (Tenth edition). McGraw-Hill.
34. Gaber, S. A., Shahat, H. A., Alkhateeb, I. A., Al Hasan, S. A., Alqatam, M. A., Almughyirah, S. M., & Kamel, M. K. (2023). Faculty members’ awareness of artificial intelligence and its relationship to technology acceptance and digital competencies at King Faisal University. International Journal of Learning, Teaching and Educational Research, 22(7), 473-496. https://doi.org/10.26803/ijlter.22.7.25
35. Galindo-Domínguez, H., & Bezanilla, M. J. (2021). Digital competence in the training of pre-service teachers: Perceptions of students in the degrees of early childhood education and primary education. Journal of Digital Learning in Teacher Education, 37(4), 262-278. https://doi.org/10.1080/21532974.2021.1934757
36. Galindo-Domínguez, H., Delgado, N., Campo, L., & Losada, D. (2024). Relationship between teachers’ digital competence and attitudes towards artificial intelligence in education. International Journal of Educational Research, 126, 1-13. https://doi.org/10.1016/j.ijer.2024.102381
37. Garzón Artacho, E., Martínez, T. S., Ortega Martin, J. L., Marin Marin, J. A., & Gomez Garcia, G. (2020). Teacher training in lifelong learning—The importance of digital competence in the encouragement of teaching innovation. Sustainability, 12(7), 2852. https://doi.org/10.3390/su12072852
38. Guan, L., Zhang, Y., & Gu, M. M. (2025). Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. Computers and Education: Artificial Intelligence, 8, 100341. https://doi.org/10.1016/j.caeai.2024.100341
39. Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & society, 34, 47-54. https://doi.org/10.1007/s00146-017-0693-8
40. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533
41. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504-526. https://doi.org/10.1007/s40593-021-00239-1
42. Hornberger, M., Bewersdorff, A., Schiff, D. S., & Nerdel, C. (2025). A multinational assessment of AI literacy among university students in Germany, the UK, and the US. Computers in Human Behavior: Artificial Humans, 4, 100132. https://doi.org/10.1016/j.chbah.2025.100132
43. Hossain, Z., Çelik, Ö., & Hınız, G. (2025). Exploring EFL students’ AI literacy in academic writing: Insights into familiarity, knowledge and ethical perceptions. Journal of Theoretical Educational Science, 18(1), 157–181. https://doi.org/10.30831/akukeg.1538011
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: A Multidisciplinary Journal, 6(1), 1-55, https://doi.org/10.1080/10705519909540118
45. Hu, X., Gong, W., & Cortazzi, M. (2025). Researching China's pre‐service teachers' perceptions of AI education for K‐12: An elicited metaphor analysis. European Journal of Education, 60(2), e70079. https://doi.org/10.1111/ejed.70079
46. Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 206-217. https://doi.org/10.36941/ajis-2021-0077
47. Huck, S. W. (2012). Reading statistics and research (6th Edition). Pearson Education.
48. IBM Corp. (2013). IBM SPSS Statistics for Windows (Sürüm 22.0) [Computer software]. IBM Corp.
49. JASP Team. (2025). JASP (Sürüm 0.95.3) [Computer software].
50. Kalnina, D., Nīmante, D., & Baranova, S. (2024). Artificial intelligence for higher education: Benefits and challenges for pre-service teachers. Frontiers in Education, (9), 1501819. https://doi.org/10.3389/feduc.2024.1501819
51. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
52. Karakuş, G. ve Ocak, G. (2019). Öğretmen adaylarının dijital okuryazarlık öz-yeterlik becerilerinin farklı değişkenler açısından incelenmesi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 21(1), 129-147. https://doi.org/10.32709/akusosbil.466549
53. Karasar, N. (2020). Bilimsel araştırma yöntemleri (35. Basım). Nobel.
54. Kazancı Tınmaz, A. ve Doğan, F. (2026). Dijital araçların öğretim süreçlerine etkisi: Teknolojik pedagojik alan bilgisi çerçevesinde öğretmen görüşlerinin incelenmesi. Batı Anadolu Eğitim Bilimleri Dergisi, 17(1), 513-553. https://doi.org/10.51460/baebd.1704987
55. Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, 100398.https://doi.org/10.1016/j.caeai.2025.100398
56. Lacuna, J. R. (2025). Exploring the readiness of pre-service teachers for AI integration in Philippine education. International Journal of Research and Innovation in Social Science, 9(3), 4907-4924. https://doi.org/10.47772/IJRISS.2025.90300392
57. Li, M., Wang, Y., & Yang, X. (2025). Can generative AI chatbots promote second language acquisition? A meta-analysis. Journal of Computer Assisted Learning, 41, e70060. https://doi.org/10.1111/jcal.70060
58. Lim, E. M. (2023). The effects of pre-service early childhood teachers’ digital literacy and self-efficacy on their perception of AI education for young children. Education and Information Technologies, 28(10), 12969-12995. https://doi.org/10.1007/s10639-023-11724-6
59. Lipovec, A., & Flogie, A. (2023, June). Empowering future teachers: Unveiling their attitudes and knowledge about AI in Slovenian K-12 education. In 12th Mediterranean Conference on Embedded Computing (MECO) (ss. 1-4). IEEE. https://doi.org/10.1109/MECO58584.2023.10155010
60. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
61. Long, D., & Magerko, B. (2020, Nisan). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (ss. 1-16). https://doi.org/10.1145/3313831.3376727
62. López-Meneses, E., Sirignano, F. M., Vázquez-Cano, E., & RamírezHurtado, J. M. (2020). University students’ digital competence in three areas of the DigCom 2.1 model: A comparative study at three European universities. Australasian Journal of Educational Technology, 36(3), 69-88. https://doi.org/10.14742/ajet.5583
63. Ma, N., & Z. Zhong. (2025). A meta-analysis of the impact of generative artificial intelligence on learning outcomes. Journal of Computer Assisted Learning, 41(5), 1-21 https://doi.org/10.1111/jcal.70117
64. Ma, Y., Tang, X. J., & Huang, X. (2025). AI-powered adaptive English language learning systems: Leveraging deep learning algorithms and natural language processing for personalized teaching approaches. IEEE, 13, 153189-153198. https://doi.org/10.1109/ACCESS.2025.3603602
65. Madu, C. O., & Musa, A. (2024). Lecturers’ level of awareness of artificial intelligence as correlate of their digital competence at Federal University Wukari, Nigeria. Journal of Educational Research in Developing Areas, 5(1), 59-67. https://doi.org/10.47434/JEREDA.5.1.2024.59
66. Mart, M., ve Kaya, G. (2024). Okul öncesi öğretmen adaylarının yapay zekâya yönelik tutumları ve yapay zekâ okuryazarlığı arasındaki ilişkinin incelenmesi. Edutech Research, 2(1), 91-109.
67. Merzifonluoglu, A., & Gunes, H. (2025). Shifting dynamics: Who holds the reins in decision-making with artificial intelligence tools? Perspectives of gen Z pre-service teachers. European Journal of Education, 60, e70053. https://doi.org/10.1111/ejed.70053
68. Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: A guidance for policymakers. Unesco Publishing. https://doi.org/10.54675/PCSP7350
69. Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. 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
70. Özden, M., ve Yaşar, F. Ö. (2024). Eğitim fakültesi öğrencilerinin dijital yeterlikleri. Ana Dili Eğitimi Dergisi, 12(3), 516-529. https://doi.org/10.16916/aded.1496401
71. Özden, M., Örge Yaşar, F., & Meydan, E. (2025). The relationship between pre-service teachers’ attitude towards artificial intelligence (AI) and their AI literacy. Pegem Journal of Education and Instruction, 15(3), 121–131. https://doi.org/10.47750/pegegog.15.03.13
72. Özer-Altınkaya, Z., & Yetkin, R. (2025). Exploring pre-service English language teachers’ readiness for AI-integrated language instruction. Pedagogies: An International Journal, 1-17, https://doi.org/10.1080/1554480X.2025.2451299
73. Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023). Implementing artificial intelligence in higher education: Pros and cons from the perspectives of academics. Societies, 13(5), 118. https://doi.org/10.3390/soc13050118
74. Polatgil, M., ve Güler, A. (2023). Yapay Zekâ Okuryazarlığı Ölçeğinin Türkçe’ye uyarlanması. Sosyal Bilimlerde Nicel Araştırmalar Dergisi, 3(2), 99-114. https://sobinarder.com/index.php/sbd/article/download/65/32
75. Reiss, M. J. (2021). The use of AI in education: Practicalities and ethical considerations. London Review of Education, 19(1), 1–14. https://doi.org/10.14324/LRE.19.1.05
76. Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling (3rd Edition). Routledge.
77. Seddik, M. E. (2025). The impact of AI-powered language learning tools on second language acquisition: A mixed-methods study. International Journal of Linguistics, Literature and Translation, 8(3), 269-278. https://doi.org/10.32996/ijllt.2025.8.3.30
78. Skakun, I. (2021). Digital competencies of the teacher of the future. Futurity Education, 1(2), 46-56. https://doi.org/10.57125/FED/2022.10.11.18
79. Sperling, K., Stenberg, C. J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169. https://doi.org/10.1016/j.caeo.2024.100169
80. Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146. https://doi.org/10.1016/j.indmarman.2017.12.019
81. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
82. Toker Gokce, A., Deveci Topal, A., Kolburan Geçer, A., & Eren, C. D. (2025). Investigating the level of artificial intelligence literacy of university students using decision trees. Education and Information Technologies, 30, 6765-6784. https://doi.org/10.1007/s10639-024-13081-4
83. Tomczyk, Ł. (2024). Digital competence among pre-service teachers: A global perspective on curriculum change as viewed by experts from 33 countries. Evaluation and Program Planning, 105, 102449. https://doi.org/10.1016/j.evalprogplan.2024.102449
84. Torreciba, N. D., & Alieto, E. O. (2025). Attitude, anxiety, and literacy among teacher aspirants’ embrace of artificial intelligence: Implications for practical and ethical challenges in integrating AI in education. EthAIca, 4(416), 1-21. https://doi.org/10.56294/ai2025416 United Nations Children’s Fund (UNICEF). (2021). Policy guidance on AI for children. UNICEF Office of Global Insight and Policy. https://www.unicef.org/innocenti/reports/policy-guidance-ai-children
85. United Nations Educational, Scientific and Cultural Organization (UNESCO). (2021). Draft text of the recommendation on the ethics of artificial intelligence (41 C/73). https://unesdoc.unesco.org/ark:/48223/pf0000378931
86. Van Laar, E., Van Deursen, A. J., Van Dijk, J. A., & De Haan, J. (2020). Determinants of 21st-century skills and 21st-century digital skills for workers: A systematic literature review. Sage Open, 10(1). https://doi.org/10.1177/21582440199001
87. Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
88. Yeniçeri, Ü., ve Kenan, A. (2025). Öğretmen adaylarının yapay zekâya yönelik tutum ve kaygılarının çeşitli değişkenler açısından betimsel analizi. Ege Eğitim Dergisi, 26(2), 226-245. https://doi.org/10.12984/egeefd.1625648
89. Yoon, S. H. (2022). Gender and digital competence: Analysis of pre-service teachers’ educational needs and its implications. International Journal of Educational Research, 114, 101989, https://doi.org/10.1016/j.ijer.2022.101989
90. Yoon, S. H., & B. S. Jang. (2024). A study on measuring AI Literacy of pre-service secondary school teachers. Journal of Practical Engineering Education, 16(5), 725–731. https://doi.org/10.14702/JPEE.2024.725
91. Yükseköğretim Kurulu. (2016). Yükseköğretim Kurumları Bilimsel Araştırma ve Yayın Etiği Yönergesi. https://www.yok.gov.tr/documents/documents/68fb63b8b2511.pdf
92. Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(49), 1-22. https://doi.org/10.1186/s41239-023-00420-7
93. Zhang, C., Hu, M., Wu, W., Kamran, F., & Wang, X. (2024). Unpacking perceived risks and AI trust influences pre-service teachers' AI acceptance: A structural equation modeling-based multi-group analysis. Educ. Inf. Technol., 30, 2645-2672. https://doi.org/10.1007/s10639-024-12905-7