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Journals / Iğdır Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Sayı: 14

Generative Artificial Intelligence and Economics Education: What Awaits Us?

Üretken Yapay Zekâ ve İktisat Eğitimi: Bizi Ne Bekliyor?

Aras Yolusever(İSTANBUL KÜLTÜR ÜNİVERSİTESİ)0000-0001-9810-2571
Pages
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Publication Date
2026-07-02
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AI Summary & Key Takeaways

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

The study aims to provide policy recommendations on the future of economics education and to create a roadmap for instructors, curriculum designers, and decision-makers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rapid proliferation of generative artificial intelligence technologies in recent years has paved the way for fundamental transformations in education.
📊
Key Findings & Data:The findings demonstrate that generative AI offers significant opportunities in areas such as personalized learning, instant feedback, access to computational economics applications, and visualization of complex models.
💡
Conclusion & Impact:The study aims to provide policy recommendations on the future of economics education and to create a roadmap for instructors, curriculum designers, and decision-makers.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Üretken yapay zekâ · iktisat eğitimi · büyük dil modelleri

Abstract

The rapid proliferation of generative artificial intelligence technologies in recent years has paved the way for fundamental transformations in education. The rise of large language models such as ChatGPT, Claude, and Gemini compels higher education institutions to reconsider their pedagogical approaches. This study examines the potential effects of generative AI on economics education from a conceptual and critical perspective. Using document analysis as a qualitative research method, the relevant academic literature, institutional reports, and current applications are evaluated. The findings demonstrate that generative AI offers significant opportunities in areas such as personalized learning, instant feedback, access to computational economics applications, and visualization of complex models. On the other hand, serious challenges arise, including academic integrity, erosion of critical thinking skills, hallucination risk, and digital inequality. The study aims to provide policy recommendations on the future of economics education and to create a roadmap for instructors, curriculum designers, and decision-makers.

Özet

Üretken yapay zekâ teknolojilerinin son yıllarda hızla yaygınlaşması, eğitim alanında köklü dönüşümlerin önünü açmıştır. ChatGPT, Claude ve Gemini gibi büyük dil modellerinin yükselişi, yükseköğretim kurumlarının pedagojik yaklaşımlarını yeniden gözden geçirmesini zorunlu kılmaktadır. Bu çalışmada üretken yapay zekânın iktisat eğitimi üzerindeki potansiyel etkileri kavramsal ve eleştirel bir bakış açısıyla incelenmektedir. Nitel araştırma yöntemlerinden doküman incelemesi tekniği kullanılarak ilgili akademik literatür, kurumsal raporlar ve güncel uygulamalar değerlendirilmiştir. Bulgular, üretken yapay zekânın kişiselleştirilmiş öğrenme, anlık geri bildirim, hesaplamalı iktisat uygulamalarına erişim ve karmaşık modellerin görselleştirilmesi gibi alanlarda önemli fırsatlar sunduğunu göstermektedir. Diğer yandan akademik dürüstlük, eleştirel düşünme becerilerinde aşınma, halüsinasyon riski ve dijital eşitsizlik gibi ciddi zorluklar da gündeme gelmektedir. Çalışma, iktisat eğitiminin geleceğine ilişkin politika önerileri sunarak öğretim üyeleri, müfredat tasarımcıları ve karar alıcılar için bir yol haritası oluşturmayı amaçlamaktadır.

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Cite This Article

Yolusever, A. (2026). Generative Artificial Intelligence and Economics Education: What Awaits Us?. Iğdır Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 14. https://doi.org/10.58618/igdiriibf.1946886

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References (41)

  1. Acquah, B. Y. S., Arthur, F., Salifu, I., Mensah, E., Opoku, E., Nortey, S. A., & Tetteh, S. A. (2025). Modelling economics students' use of ChatGPT and academic performance: Insights from self-determination theory and epistemic curiosity.Discover Artificial Intelligence, 5. https://doi.org/10.1007/s44163-025-00373-y
  2. Arum, R., Calderon Leon, M., Li, X., & Lopes, J. (2025). ChatGPT Early Adoption in Higher Education: Variation in Student Usage, Instructional Support, and Educational Equity. AERA Open, 11.
  3. Avrupa Komisyonu. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
  4. Barcaui, A. (2025). ChatGPT as a Cognitive Crutch: Evidence from a Randomized Controlled Trial on Knowledge Retention. Social Sciences & Humanities Open, 12, 102287. https://doi.org/10.1016/j.ssaho.2025.102287
  5. Besalti, M. (2025). Harnessing self-control and AI: Understanding ChatGPT's impact on academic wellbeing. Behavioral Sciences, 15(9), 1181. https://doi.org/10.3390/bs15091181
  6. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S.,. & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
  7. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40.
  8. Bowles, S., & Carlin, W. (2020). What students learn in economics 101: Time for a change. Journal of Economic Literature, 58(1), 176–214.
  9. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E.,. & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712
  10. Contractor, Z., & Reyes, G. (2025). Generative AI in Higher Education: Evidence from an Elite College. IZA Discussion Papers No. 18055, IZA Institute of Labor Economics.
  11. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239.
  12. Cowen, T., & Tabarrok, A. (2023). How to learn and teach economics with large language models, including GPT. GMU Working Paper in Economics No. 23-18.
  13. Çütcü, İ. (2014). İktisat eğitiminin temel sorunları ve çözüm önerileri: Adıyaman Üniversitesi üzerine bir uygulama. Electronic Journal of Vocational Colleges, 4(1), 110–122. Digital Education Council. (2024). Global AI Faculty Survey 2024. https://www.digitaleducationcouncil.com/post/what-students-want-key-results-from-dec-global-ai-student-survey-2024
  14. Earle, J., Moran, C., & Ward-Perkins, Z. (2017). The econocracy: The perils of leaving economics to the experts. Manchester University Press.
  15. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702), 1306–1308. https://doi.org/10.1126/science.adj0998 Higher Education Policy Institute (HEPI). (2025). Student generative AI survey 2025 (Policy Note 61). https://www.hepi.ac.uk/reports/student-generative-ai-survey-2025/
  16. Eisenbardt, M., Kurtz, G., Gafni, R., Leiba, M., Dettmer, S., Mullins, R., Siegert, I., Eisenbardt, T., & Gal, E. (2025). Students' readiness and ethical considerations to adopt generative AI: An international comparative study. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09934-7 Geerling, W., Mateer, G. D., Wooten, J., & Damodaran, N. (2023). ChatGPT has aced the Test of Understanding in College Economics: Now what? American Economist, 68(2), 233–245.
  17. 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. https://doi.org/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, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  18. Khan, S. (2024). Brave new words: How AI will revolutionize education (and why that's a good thing). Viking.
  19. Korinek, A. (2023). Generative AI for economic research: Use cases and implications for economists. Journal of Economic Literature, 61(4), 1281–1317. https://doi.org/10.1257/jel.20231736
  20. Krause, S., Dalvi, A., & Zaidi, S. K. (2025). Generative AI in education: Student skills and lecturer roles. arXiv. https://doi.org/10.48550/arXiv.2504.19673
  21. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  22. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N.,. & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
  23. Livberber, T., & Ayvaz, S. (2023). The impact of artificial intelligence in academia: Views of Turkish academics on ChatGPT. Heliyon, 9(9), e19688. https://doi.org/10.1016/j.heliyon.2023.e19688
  24. Nakavachara, V., Potipiti, T., & Chaiwat, T. (2024). Experimenting with generative AI: Does ChatGPT really increase everyone's productivity? Chulalongkorn University Faculty of Economics Working Paper. OECD. (2025). How do people experience new technologies and generative AI? OECD Digital Well-being Hub (Cisco iş birliğiyle). https://www.oecd.org/en/publications/how-do-people-experience-new-technologies-and-generative-ai_49b8d10e-en.html
  25. Oğan, E. (2025). Üniversite Öğrencilerinin Yapay Zekâ Okuryazarlığı Üzerine Bir Araştırma. Üçüncü Sektör Sosyal Ekonomi Dergisi, 60(3), 2670–2686. https://doi.org/10.63556/tisej.2025.1565
  26. Özçelik, Ö., & Özcan, S. E. (2017). Lisans seviyesinde iktisat eğitiminde temel sorunlar ve çözüm önerileri. Kırklareli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(5), 192–202.
  27. Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), 1–25.
  28. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., Iahad, N. A., Abdulla, A. A., Akopyan, A., Segura, M. W. A., AlHumaid, J., Allam, M. F., Alló, M., Andoh, R. P. K., Andronic, O., Arthur, Y. D., Aydın, F., Badran, A.,Balbontín-Alvarado, R., Saad, H. B., … & Aristovnik, A. (2025). Higher education students' perceptions of ChatGPT: A global study of early reactions. PLOS One, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
  29. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org
  30. Uslu, B. (2023). Üniversitelerde yapay zekanın kullanım alanları: Potansiyel yararları ve olası zorluklar. Eğitimde Kuram ve Uygulama, 19(2), 227–239. https://doi.org/10.17244/eku.1355304
  31. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,. & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
  32. Webtekno. (2025). 2025'te Türkiye'de en çok kullanılan yapay zekâlar. https://www.webtekno.com/2025-turkiye-en-cok-kullanilan-yapay-zeka-h209743.html
  33. Wecks, J. O., Voshaar, J., Plate, B. J., & Zimmermann, J. (2024). Generative AI usage and exam performance. University of Bremen Working Paper.
  34. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S.,. & Fedus, W. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research.
  35. Yeadon, W., Inyang, O. O., Mizouri, A., Peach, A., & Testrow, C. P. (2023). The death of the short-form physics essay in the coming AI revolution. Physics Education, 58(3), 035027.
  36. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S.,. & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv. https://doi.org/10.48550/arXiv.2108.07258
  37. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E.,. & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv. https://doi.org/10.48550/arXiv.2303.12712
  38. 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. https://doi.org/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, 102274. https://doi.org/10.1016/j.lindif.2023.102274
  39. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N.,. & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
  40. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,. & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
  41. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S.,. & Fedus, W. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research.