Journals / Toplum ve Kültür Araştırmaları Dergisi / 2026 / Sayı: 17

Algorithmic Habitus and the Future of Education: Pedagogical, Ethical, and Societal Implications of AI

Algoritmik Habitus ve Eğitimin Geleceği: Yapay Zekânın Pedagojik, Etik ve Toplumsal Etkileri

Emine Babaoğlan(YOZGAT BOZOK UNIVERSITY)0000-0002-1151-191X
Ejder Çelik(YOZGAT BOZOK UNIVERSITY)0000-0001-5948-0609
Pages
1–26
Publication Date
2026-06-21
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

Current policy trends in teacher training, AI literacy, and data ethics are evaluated using the Turkish example.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to address the role and future position of artificial intelligence in education within a multidimensional framework.
📊
Key Findings & Data:The main objective of the research is to examine the pedagogical, ethical, epistemological, and professional impacts of AI-assisted educational applications with a holistic approach and to discuss how these technologies can be integrated into educational processes in a balanced way.
💡
Conclusion & Impact:Current policy trends in teacher training, AI literacy, and data ethics are evaluated using the Turkish example.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Eğitimde yapay zeka · Eğitim etiği · Kişiselleştirilmiş öğrenme

Abstract

This study aims to address the role and future position of artificial intelligence in education within a multidimensional framework. The main objective of the research is to examine the pedagogical, ethical, epistemological, and professional impacts of AI-assisted educational applications with a holistic approach and to discuss how these technologies can be integrated into educational processes in a balanced way. The study analyzes the effects of AI on personalized learning, measurement and evaluation, feedback mechanisms, and educational management; it also evaluates the transformations that may occur in the pedagogical role of the teacher. The text emphasizes that the flexibility, data-driven decision-making, and individualized learning opportunities offered by AI present significant opportunities; however, these opportunities must be evaluated together with serious risks such as infrastructure inequalities, algorithmic bias, data privacy, transparency, and cultural homogenization. In particular, the potential of AI to invisibly shape learning processes is discussed through concepts such as "algorithmic black box," "algorithmic habitus," and data-driven guidance. The study also addresses the effects of AI on education policies in light of national and international policy documents; The applicability of ethical frameworks is questioned within the context of approaches by organizations such as UNESCO, OECD, and the World Bank. Current policy trends in teacher training, AI literacy, and data ethics are evaluated using the Turkish example.

Özet

Bu çalışma, yapay zekânın eğitim alanındaki rolünü ve gelecekteki konumunu çok boyutlu bir çerçevede ele almayı amaçlamaktadır. Araştırmanın temel amacı, yapay zekâ destekli eğitim uygulamalarının pedagojik, etik, epistemik ve mesleki etkilerini bütüncül bir yaklaşımla incelemek ve bu teknolojilerin eğitim süreçlerine nasıl dengeli biçimde entegre edilebileceğini tartışmaktır. Çalışmada yapay zekânın kişiselleştirilmiş öğrenme, ölçme ve değerlendirme, geri bildirim mekanizmaları ve eğitim yönetimi üzerindeki etkileri analiz edilmekte; aynı zamanda öğretmenin pedagojik rolünde ortaya çıkabilecek dönüşümler değerlendirilmektedir. Metin, yapay zekânın sunduğu esneklik, veriye dayalı karar alma ve bireyselleştirilmiş öğrenme olanaklarının önemli fırsatlar sunduğunu; ancak bu fırsatların altyapı eşitsizlikleri, algoritmik önyargı, veri gizliliği, şeffaflık ve kültürel homojenleşme gibi ciddi risklerle birlikte değerlendirilmesi gerektiğini vurgulamaktadır. Özellikle “algoritmik kara kutu”, “algoritmik habitus” ve veri temelli yönlendirme gibi kavramlar üzerinden, yapay zekânın öğrenme süreçlerini görünmez biçimde şekillendirme potansiyeli tartışılmaktadır. Çalışmada ayrıca, ulusal ve uluslararası politika belgeleri ışığında yapay zekânın eğitim politikaları üzerindeki etkileri ele alınmakta; UNESCO, OECD ve Dünya Bankası gibi kuruluşların yaklaşımları bağlamında etik çerçevelerin uygulanabilirliği sorgulanmaktadır. Türkiye örneği üzerinden öğretmen eğitimi, yapay zekâ okuryazarlığı ve veri etiği konularındaki güncel politika yönelimleri değerlendirilmektedir.

🔖

Cite This Article

Babaoğlan, E., Çelik, E. (2026). Algorithmic Habitus and the Future of Education: Pedagogical, Ethical, and Societal Implications of AI. Toplum ve Kültür Araştırmaları Dergisi, Sayı: 17, 1–26. https://doi.org/10.48131/jscs.1942303

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (35)

  1. Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
  2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165
  3. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171–4186. https://arxiv.org/abs/1810.04805
  4. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., … Houlsby, N. (2021). An image is worth 16×16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2010.11929
  5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org/
  6. Grand View Research. (2024). AI in education market size & share industry report, 2030. https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-education-market-report
  7. Hao, Y., Romano, J. D., & Moore, J. H. (2022) Knowledge-guided deep learning models of drug toxicity improve interpretation. Patterns, 3(9), 1-15. DOI: 10.1016/j.patter.2022.100565
  8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://arxiv.org/abs/1512.03385
  9. Heffernan, N. T., & Heffernan, C. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://link.springer.com/article/10.1007/s40593-014-0024-x
  10. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://www.researchgate.net/publication/13853244_Long_Short-Term_Memory/link/5700e75608aea6b7746a0624/download
  11. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://www.researchgate.net/publication/332180327_Artificial_Intelligence_in_Education_Promise_and_Implications_for_Teaching_and_Learning
  12. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. 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
  13. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://www.researchgate.net/publication/277636218_Effectiveness_of_Intelligent_Tutoring_Systems_A_Meta-Analytic_Review
  14. Livingstone, S. (2014). Developing social media literacy: How children learn to interpret risky opportunities on social network sites. Communications, 39(3), 283–303. https://eprints.lse.ac.uk/62129/1/Developing%20social%20media%20literacy.pdf
  15. Long, P., &Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30–40. https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education
  16. Luckin, R. (2017). Enhancing learning and teaching with technology: What the research says. UCL Institute of Education Press.
  17. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press. https://discovery.ucl.ac.uk/id/eprint/10178695/1/Machine%20Learning%20and%20Human%20Intelligence.pdf
  18. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. file:///C:/Users/Probook/Downloads/PearsonIntelligenceUnleashedFINAL.pdf
  19. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://www.apa.org/pubs/journals/features/edu-a0037123.pdf
  20. Millî Eğitim Bakanlığı. (2025). Yapay zekâ stratejisi ve eylem planı (2025–2029). YEĞİTEK. https://yegitek.meb.gov.tr/meb_iys_dosyalar/2025_07/19150909_egitimde_yz_politika_belgesi_18072025_en.pdf
  21. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
  22. Molina, E., Cobo, C., Pineda, J., & Rovner, H. (2024). AI revolution in education: What you need to know. In Digital Innovations in Education. Worl Bank Document.
  23. Pane, J. F., Steiner, E. D., Baird, M. D., & Hamilton, L. S. (2015). Continued progress: Promising evidence on personalized learning. RAND Corporation. https://www.rand.org/pubs/research_reports/RR1365.html
  24. Pasquale, F. (2015). The black box society: The secret algorithms that control money and ınformation. Harvard University Press.
  25. Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A Modern approach (4th ed.). Pearson. http://lib.ysu.am/disciplines_bk/efdd4d1d4c2087fe1cbe03d9ced67f34.pdf
  26. Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://www.researchgate.net/publication/220017728_Focus_on_Formative_Feedback
  27. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., … Hassabis, D. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://www.researchgate.net/publication/292074166_Mastering_the_game_of_Go_with_deep_neural_networks_and_tree_search
  28. Soundararaj, G. (2022). The manifesto for teaching online. open learning: The Journal of Open, Distance and e-Learning, 37(1), 102–104. https://doi.org/10.1080/02680513.2021.2014800
  29. The World Bank (2024). 2024 Digital innovations in education: AI revolation in education, International Bank for Reconstruction and Development. https://documents1.worldbank.org/curated/en/099734306182493324/pdf/IDU152823b13109c514ebd19c241a289470b6902.pdf
  30. U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
  31. UNESCO. (2021). AI and education: Guidance for policy-makers. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000376709
  32. Williamson, B. (2017). Big data in education: the digital future of learning, policy and practice. Sage.
  33. 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
  34. Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S. (2022). Artificial intelligence applications in K-12 education: A systematic literature review. IEEE Open Access, 10. https://ieeexplore.ieee.org/document/9785805
  35. Zawacki-Richter, O., Marín, 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. https://doi.org/10.1186/s41239-019-0171-0