Yükseköğretimde Dijital Dönüşüme Karşı Öğrenci Direncinin Aşılmasında Yapay Zekâ Okuryazarlığının Katalizör Rolü

Öz

Bu çalışma, öğrencilerin değişime direnci ve yapay zekâ okuryazarlığının yükseköğretimde yapay zekaya (YZ) karşı tutumlarını nasıl etkilediğini araştırmaktadır. Bu çalışmada, dijital dönüşüm sürecinde karşılaşılan psikolojik engellerin aşılmasına katkı sağlamak amacıyla, özellikle değişime direncin yapay zekâya yönelik tutumlar üzerindeki olumsuz etkilerinde yapay zekâ okuryazarlığının aracılık rolünün belirlenmesi amaçlanmaktadır. Nicel, kesitsel bir araştırma tasarımı kullanılarak, Türkiye’deki 564 üniversite öğrencisinden yapılandırılmış bir anket aracılığıyla veri toplanmıştır. Çalışmada, önerilen hipotezleri test etmek için Yapısal Eşitlik Modeli (YEM) ve Hayes’in PROCESS Model 4 kullanılmıştır. Bulgular, değişime direncin artışının hem YZ okuryazarlığını hem de YZ’ye karşı tutumları önemli ölçüde ve olumsuz etkilediğini ortaya koymaktadır. Ayrıca, YZ okuryazarlığı öğrencilerin YZ tutumları üzerinde olumlu bir etkiye sahiptir ve değişime direnç ile tutum arasındaki ilişkiyi kısmen aracılık etmektedir. Bütünleşik bir RTC→AIL→AIA yolu deneysel olarak kurularak ve AIL'yi dirençle ilişkili belirsizliği algılanan kontrole dönüştüren geliştirilebilir bir yetkinlik olarak belirleyerek özgün bir katkı sunulmaktadır. Böylece tutumların bilgiye dayalı mekanizmalar aracılığıyla nasıl iyileştirildiği açıklığa kavuşturulmaktadır. Sonuçlar, bilgiye dayalı güven ve bilişsel hazırlığın, yapay zekâya yönelik olumlu tutumların güçlenmesine anlamlı katkı sunduğunu göstermektedir. Bu çalışma, değişime direnç, YZ okuryazarlığı ve YZ’ye karşı tutumları birbirine bağlayan entegre bir kavramsal çerçeve sunarak literatüre katkıda bulunmaktadır. Pratik açıdan, tasarım odaklı çıkarımlar eğitim programlaması için belirtilmiş olup, etik-gizlilik-önyargı içeriklerinin ve açıklanabilirlik odaklı geri bildirimlerin entegre edilmesinin arabuluculuk mekanizmasını rutin uygulamaya dönüştürebileceğini ve dirençle ilişkili sürtüşmeleri azaltmaya yardımcı olabileceğini göstermektedir. Dijital dönüşüm geçiren eğitim ortamlarında sürdürülebilir öğrenme davranışlarını teşvik etmek için bilişsel yetkinliklerin ele alınmasının gerekli olduğunu vurgulamaktadır.

Anahtar Kelimeler

• Yükseköğretim
• Dijital Dönüşüm
• YZ Okuryazarlığı
• Değişime Direnç
• Yapay Zekâ Tutumu

Artificial Intelligence Literacy as a Catalyst for Overcoming Student Resistance to Digital Transformation in Higher Education

Abstract

This study investigates how students’ resistance to change and AI literacy influence their attitudes toward artificial intelligence (AI) in higher education. With the aim of contributing to overcoming psychological barriers encountered in the digital transformation process, this study examines the mediating role of AI literacy in the negative effect of resistance to change on students’ attitudes toward AI. Using a quantitative, cross-sectional research design, data were collected from 564 university students in Türkiye through a structured questionnaire. To test the proposed hypotheses, the research utilized Structural Equation Modeling (SEM) alongside Hayes’ PROCESS Model 4 for mediation analysis. The findings reveal that increased resistance to change significantly and negatively impacts both AI literacy and attitudes toward AI. Furthermore, AI literacy has a positive effect on students’ attitudes toward AI and partially mediates the relationship between resistance to change and attitude. An original contribution is offered by empirically establishing an integrated RTC→AIL→AIA pathway and by specifying AIL as a developable competence that converts resistance-related ambiguity into perceived control, thereby clarifying how attitudes are improved through knowledge-based mechanisms. The results show that knowledge-based trust and cognitive preparation significantly contribute to the strengthening of positive attitudes towards AI. This research contributes to the literature by presenting an integrated conceptual framework linking resistance to change, AI literacy, and attitude toward AI. In practical terms, design-addressable implications are specified for educational programming, indicating that embedding ethics–privacy–bias content and explainability-oriented feedback can translate the mediation mechanism into routine practice and help reduce resistance-related frictions. It emphasizes that addressing cognitive competencies is essential in promoting sustainable learning behaviors in digitally transforming educational environments.

Keywords

• Higher Education
• Digital Transformation
• AI Literacy
• Resistance to Change
• Attitude toward AI

References

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
2. Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27–58. https://doi.org/10.1146/annurev.psych.52.1.27
3. Alamäki, A., Nyberg, C., Kimberley, A., & Salonen, A. O. (2024, March). Artificial intelligence literacy in sustainable development: A learning experiment in higher education. In Frontiers in Education, 9, 1343406. https://doi.org/10.3389/feduc.2024.1343406
4. Amin, S. M., El‐Gazar, H. E., Zoromba, M. A., El‐Sayed, M. M., & Atta, M. H. R. (2025). Sentiment of nurses towards artificial intelligence and resistance to change in healthcare organizations: A mixed‐method study. Journal of Advanced Nursing, 81(4), 2087-2098. https://doi.org/10.1111/jan.16435
5. Bandura, A. (1997). Self-efficacy: The Exercise of Control. New York: W. H. Freeman.
6. Browne, M. W., & Cudeck, R. (1993). In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 136–162). Sage.
7. Çalışkan, A. (2019). Resistance to change: a study of scale adaptation. Suleyman Demirel University the Journal of Faculty of Economics and Administrative Sciences, 24(2), 237-252.
8. Castro, C. A., Zambaldi, F., & Ponchio, M. C. (2020). Cognitive and emotional resistance to innovations: concept and measurement. Journal of Product & Brand Management, 29(4), 441-455. https://doi.org/10.1108/JPBM-10-2018-2092

9. Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740
10. Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
11. Davis, 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
12. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
13. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt Brace Jovanovich College Publishers.
14. Erbey, A., Gündüz, C., & Fidan, Ü. (2025). Digitalization, Sustainability, and Radical Innovation: A Knowledge-Based Approach. Sustainability, 17(7), 2972. https://doi.org/10.3390/su17072972
15. Fornell, C., & Larcker, D. F. (1981). Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
16. Frick, N. R., Mirbabaie, M., Stieglitz, S., & Salomon, J. (2021). Maneuvering through the stormy seas of digital transformation: the impact of empowering leadership on the AI readiness of enterprises. Journal of Decision Systems, 30(2-3), 235-258. https://doi.org/10.1080/12460125.2020.1870065
17. Grassini, S. (2023). Development and validation of the AI attitudes scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
18. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage.
19. Hu, L. T., & Bentler, P. M. (1999). Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
20. Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582. https://doi.org/10.2307/20650309
21. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
22. Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
23. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
24. Li, C., Ashraf, S. F., Amin, S., & Safdar, M. N. (2023). Consequence of resistance to change on AI readiness: Mediating–moderating role of task-oriented leadership and high-performance work system in the hospitality sector. Sage Open, 13(4), 21582440231217731. https://doi.org/10.1177/21582440231217731
25. Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727
26. Oreg, S. (2003). Resistance to change: Developing an individual differences measure. Journal of Applied Psychology, 88(4), 680–693. https://doi.org/10.1037/0021-9010.88.4.680
27. Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of psychology, 156(1), 68-94. https://doi.org/10.1080/00223980.2021.2012109
28. Satici, S. A., Okur, S., Yilmaz, F. B., & Grassini, S. (2025). Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity. BMC psychology, 13(1), 1-14. https://doi.org/10.1186/s40359-025-02505-6
29. Shahid, M. K., Zia, T., Bangfan, L., Iqbal, Z., & Ahmad, F. (2024). Exploring the relationship of psychological factors and adoption readiness in determining university teachers’ attitude on AI-based assessment systems. The International Journal of Management Education, 22(2), 100967. https://doi.org/10.1016/j.ijme.2024.100967
30. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
31. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
32. Vial, G. (2021). Understanding digital transformation: A review and a research agenda. Managing digital transformation, pp. 13-66. Routledge.
33. 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
34. Yadrovskaia, M., Porksheyan, M., Petrova, A., Dudukalova, D., & Bulygin, Y. (2023). About the attitude towards artificial intelligence technologies. In E3S Web of Conferences (Vol. 376, p. 5025). EDP Sciences. https://doi.org/10.1051/e3sconf/202337605025