Yapay Zekâ Eğitim Sistemlerinde Eleştirel Düşünmeyi Tehdit Ediyor

Yıl 2025, Cilt: 15 Sayı: 2, 157 - 164, 31.08.2025

Mahmut Özer , Hande Tanberkan , Matjaz Perc

Öz

Bu çalışmada eğitimde metin üreten ve özetleyen, dilleri çeviren ve görsel içerik oluşturan araçlar yoluyla yapay zekânın giderek artan kullanımının öğrencilerin eleştirel düşünme becerileri üzerindeki etkisini inceliyoruz. Bu teknolojiler kişiselleştirilmiş öğrenmeyi geliştirse, değerlendirme stratejilerini çeşitlendirse ve veriye dayalı politika kararlarını desteklese de, öğrenme sürecine entegrasyonlarının istenmeyen bilişsel sonuçlar doğurduğunu savunuyoruz. Özellikle, öğrencilerin temel görevleri yapay zekâ sistemlerine devrettiğinde, bilişsel yükleri öyle bir şekilde azalıyor ki bu durum hem hafıza kalıcılığını zayıflatıyor hem de içeriğe yönelik aktif katılımı azaltıyor. Bu değişim, öğrencilerin entelektüel görevleri kendi yerlerine yapay zekânın gerçekleştirmesine giderek daha fazla güvenmesiyle bir aşırı bağımlılık örüntüsünü teşvik ediyor. Sonuç olarak, eleştirel düşünme, bilgiyi sorgulama ve kaynakları değerlendirme becerileri zamanla zayıflıyor. Bu gelişen bağımlılığı, eleştirel düşünme açısından orta ve uzun vadeli bir tehdit olarak vurguluyor ve üretken yapay zekânın eğitimde nasıl kullanıldığının yalnızca faydaları açısından değil, temel bilişsel süreçler üzerindeki etkileri bakımından da dikkatlice değerlendirilmesi gerektiğini savunuyoruz. Son olarak, bu etkileri azaltmaya ve yapay zekâ yoğun öğrenme ortamlarında öğrencilerin eleştirel kapasitesini korumaya yönelik stratejiler öneriyoruz.

Anahtar Kelimeler

Yapay zekâ , eğitim sistemi , eleştirel düşünme , bilişsel yük , hafıza

Artificial Intelligence Threatens Critical Thinking in Education Systems

Öz

We examine how the increasing use of artificial intelligence (AI) in education—through tools that generate and summarize text, translate languages, and produce visual content—impacts students' critical thinking. While these technologies enhance personalized learning, broaden assessment strategies, and support data-driven policy decisions, we argue that their integration into the learning process carries unintended cognitive consequences. Specifically, we show that when students offload key tasks to AI systems, their cognitive load decreases in ways that weaken memory retention and reduce active engagement with content. This shift fosters a pattern of overreliance, as students increasingly depend on AI to perform intellectual tasks in their place. As a result, their ability to think critically, question information, and evaluate sources diminishes over time. We highlight this emerging dependency as a medium- to long-term threat to critical thinking and call for a more careful evaluation of how generative AI is used in education—not only in terms of its benefits, but also its influence on core cognitive processes. Finally, we propose targeted strategies to mitigate these effects and preserve students' critical capacities in AI-rich learning environments

Anahtar Kelimeler

Artificial intelligence , education system , critical thinking , cognitive load , memory

Kaynakça

• Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(311), doi: 10.1057/s41599-023-01787-8
• Ambele, R. M., Kaijage, S. F., & Dida, M. A., Trojer, L., & Kyando N. W. (2022). A review of the development trend of personalized learning technologies and its applications. International Journal of Advances in Scientific Research and Engineering, 8 (11), 75–91. doi: 10.31695/JIASRE.2022.8.11.9
• Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. Retrieved from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
• Athaluri, S. A., Manthena, S. V., Kesapragada, V. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023). Exploring the boundaries of reality: investigating the phenomenon of artificial intelligence hallucination in scientific writing through ChatGPT references. Cureus, 15(4). doi: 10.7759/Cureus.37432.
• Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. Retrieved from https://digitalcommons.nri.edu/cba_facpubs/548.
• Berberette E, Hutchins J, Sadovnik A. Redefining “Hallucination”in LLMs:Towards a psychology-informed framework for mitigating misinformation. arXiv. 2024:2402–01769v1.
• Chiu, T. K. F. (2023). 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. doi: 10.1080/10494820.2023.2253861
• Ghosh, S., Venkit, P. N., Gautam, S., Wilson, S., & Caliskan, A. (2024). Do generative AI models output harm while representing non-Western cultures: Evidence from a community-centered approach. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (Vol. 7, pp. 476-489).
• Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1). doi: 10.1126/sciadv.aao5580
• Gangadharbatla, H. (2022). The role of AI attribution knowledge in the evaluation of artwork. Empirical Studies of the Arts, 40(2), 125–142. doi: 10.1177/0276237421994697
• Gerlich, M. (2025). AI Tools in Society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. doi: 10.3390/soc15010006
• Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. doi: 10.3390/educsci13070692
• Heaton, D., Nichele, E., Clos, J., & Fischer, J. E. (2023). “The algorithm will screw you”: Blame, social actors and the 2020 A Level results algorithm on Twitter. PLoS One, 18(7). doi: 10.1371/journal.pone.0288662
• Idowu, J. A. (2024). Debiasing education algorithms. International Journal of Artificial Intelligence in Education, 34, 1510–1540. doi: 10.1007/s40593-023-00389-4
• Illkhan, S., Özer, M., Tanberkan, H., & Bozkurt, V. (2024). How to mitigate the risks of deployment of artificial intelligence in medicine? Turkish Journal of Medical Science, 54(3), 483-492. doi: 10.55730/1300-0144.5814
• Illkhan, S., Özer, M., Perc, M., Tanberkan, H., & Ayhan, Y. (2025). Complementary use of artificial intelligence in healthcare. Medical Journal of Western Black Sea, 9(1), 7-17. doi: 29058/njwbs.1620035
• Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., … Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. doi: 10.1145/3571730
• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103. doi: 10.1016/j.lindif.2023.102274
• Khan, R. A., Jawaid, M., Khan, A. R., Sajjad, M. (2023). ChatGPT—Reshaping medical education and clinical management. Pakistan Journal of Medical Sciences, 39(2), 605–607. doi: 10.12669/pjms.392.7653
• Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., … Maes, P. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv:2506.08872. doi: 10.48550/arXiv.2506.08872
• Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14–19. doi: 10.1111/j.1740-9713.2016.00960.x
• Mohammadkarimi, E., & Omar, J. A. (2025). Does artificial intelligence impede critical thinking? A case study of Iranian university students. Journal of Applied Learning & Teaching, 8(2), 1-9. doi: 10.37074/jait.2025.8.2.3
• Obermeyer, Z., Powers, B., Vogel, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.doi: 10.1126/science.aax2342
• O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books.
• Özer, M. & Suna, H. E. (2022). The relationship between school socioeconomic composition and academic achievement in Turkiye. Journal of Economy Culture and Society, 66, 17–27. doi: 10.26650/JECS2022-1116979
• Özer, M. (2024a). Potential benefits and risks of artificial intelligence in education. Barton University Journal of Faculty of Education, 13(2), 232-244. doi: 10.14686/1416087
• Özer, M. (2024b). Impact of ChatGPT on Scientific Writing. The Journal of Humanity and Society, 14(3), 210-217. doi: 10.12658/E0002
• Özer, M. (2024c). Is artificial intelligence hallucinating?. Turkish Journal of Psychiatry, 35(4), 333-335. doi: 10.5080/u27587
• Özer, M. (2024d). Dynamics of the meritocracy trap and artificial intelligence. International Journal of Management Economics and Business, 20(3), 845-869. doi: 10.17130/jimeb.1524229
• Özer, M., & Perc, M. (2024). Human complementation must aid automation to mitigate unemployment effects due to AI technologies in the labor market. Reflexif Journal of Social Sciences, 5(2), 503-514. doi: 10.47613/reflexif.2024.176
• Özer, M., Perc, M., & Suna, H. E. (2024a). Artificial intelligence bias and the amplification of inequalities in the labor market. Journal of Economy, Culture and Society, 69, 159-168. doi: 10.26650/JECS2023-1415085
• Özer, M., Perc, M., & Suna, H. E. (2024b). Participatory management can help AI ethics adhere to the social consensus. Istanbul University Journal of Sociology, 44(1), 221-238. doi: 10.26650/SJ.2024.441.0001
• Özer, M. (2025). Can mathematical models be weapons of mass destruction? Reflexif Journal of Social Sciences, 6(1), 259-268. doi: 10.47613/reflexif.2025.212
• Perc, M., Özer, M., & Hojiuk, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5(61). doi: 10.1057/s41599-019-0278-x
• Qadri, R., Shelby, R., Bennett, C. L., & Denton, R. (2023). AI’s Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 506–517. doi: 10.1145/3593013.3594016
• Rudolph, J., Tan, S., & Tan, S. C. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1-22. doi: 10.37074/jait.2023.6.1.9
• Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance. Psychological Bulletin, 135(1), 1–22.
• Said, E. (1979). Orientalism. New York:Vintage Books
• Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160. doi: 10.1016/j.chb.2024.108386
• Suleyman, M. (2023). The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma. New York: Crown.
• Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M. & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economy Culture and Society, 61, 41-64. doi: 10.26650/JECS2020-0034
• Suna, H. E. & Özer, M. (2021). The impact of school tracking on secondary vocational education and training in Turkey. Hacettepe University Journal of Education, 36(4), 855-870. doi: 10.16986/HUIE.2021068158
• Suna, H. E., Özer, M. Sensoy, S., Gür, B. S., Gelbal, S., & Askar, P. (2021). Determinants of academic achievement in Turkey. Journal of Economy Culture and Society, 64, 143-162. doi: 10.26650/JECS2021-934211
• Suna, H. E. & Özer, M. (2022). The relationship of preschool attendance with academic achievement and socioeconomic status in Turkey. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 54-68. doi: 10.21031/epod.1060460
• Suna, H. E. & Özer, M. (2024). Medium- and long-term outcomes of early childhood education: experiences from Turkish large-scale assessments. Humanities and Social Sciences Communications, 11, 853. doi: 10.1057/s41599-024-03241-9
• Suna, H. E., & Özer, M. (2025). The human complimentary usage of AI and ML for fair and unbiased educational assessments. Chinese/English Journal of Educational Measurement and Evaluation, 6(1), 1-21. doi: 10.59863/YPKL4338
• Tanberkan, H., Özer, M., & Gelbal, S. (2024). Impact of artificial intelligence on assessment and evaluation approaches in education. International Journal of Educational Studies and Policy, 5(2), 139-152. doi: 10.5281/zenodo.14016103
• Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313. doi: 10.1126/science.adg7879
• Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education, 17, 12. doi: 10.1186/s41239-020-00186-z
• Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40 (5), 665–687. doi: 10.1111/ropr.12567
• Villegas-Ch, W., Arias-Navarrete, A., & Palacios-Pacheco, X. (2020). Proposal of an architecture for the integration of a Chatbot with artificial intelligence in a smart campus for the improvement of learning. Sustainability, 12(4), 1500. doi: 10.3390/su12041500
• Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D.,…Tu, Z. (2023). Document-level machine translation with large language models. arXiv:2304.02210. doi: 10.48550/arXiv.2304.02210
• Zawacki-Richter, O., Marin, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. doi: 10.1186/s41239-019-0171-0
• Zhang M, Press O, Merrill W., Liu, A., Smith, N. A. (2023). How languagemodellualucinationscansnowball. arXiv:2305–13534. doi: 10.48550/arXiv.2305.13534