Üretken Yapay Zeka ile Diyalojik Ortak Yaratım: Yükseköğretimde Paralel Düşünme Kanalları, Sezgi ve Epistemik Dönüşümün Bütünleştirilmesi

Abstract

Yapay zeka, yalnızca teknolojik bir araç olarak değil, bilgi uyuşmazlıklarının öğrenmeyi ve entelektüel gelişimi zenginleştirebileceği bir bilişsel ortak olarak ele alınmaktadır. Bu araştırma makalesi, kavramsal analiz yoluyla diyalojik pedagoji, bilişsel uyumsuzluk ve epistemik tevazu teorilerini bir araya getirmektedir. Makale, Paralel Düşünme Kanallarının eleştirel düşünmeyi, iç görü ve hesaplamalı akıl yürütme ile insan sezgisinin yenilikçi bir şekilde birleştirilmesini nasıl desteklediğini incelemektedir. Bu çalışmanın amacı, yükseköğretim bağlamında üretken yapay zekanın devrimci potansiyelini inceleyerek diyalojik ortak yaratım kavramını daha da ileriye taşımaktır. Bu çalışmada, 419 lisans ve lisansüstü öğrencinin katılımıyla nicel bir yaklaşım kullanılmıştır. Veri analizi, kavramsal model ve hipotezler, çalışmanın yapıları arasındaki ilişkileri analiz etmek üzere SmartPLS 4.1.1.8 sürümünde Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi (PLS-SEM) kullanılarak değerlendirilmiştir. Çalışmanın sonuçları, makine tarafından üretilen sonuçlar ile insan yargısı arasındaki çatışmadan kaynaklanan farklılıkların, dönüştürücü öğrenmenin tetikleyicileri olarak işlev gördüğünü göstermektedir. Sezgiyi hesaplamalı düşünmeyle birleştiren öğrenciler, belirsizliği yönetme becerilerini geliştirir, anlayışlarını genişletir ve esnek bir entelektüel dayanıklılık kazanırlar. Bilgi geliştirme sürecinde işbirlikçi bir ortak olarak bu çalışma, üretken yapay zekayı yükseköğretime entegre etmek için yeni bir çerçeve sunmaktadır. Mezunları karmaşık ve belirsiz dijital geleceğe hazırlamak amacıyla, bu yaklaşım ortak yaratım, eleştirel düşünme ve epistemik esnekliğe vurgu yapmaktadır.

Keywords

• Diyalojik Ortak Yaratım
• Epistemik Dönüşüm
• Yükseköğretim
• Üretken Yapay Zeka
• Paralel Düşünme Kanalları

Dialogic Co-Creation with Generative AI: Reconciling Parallel Thinking Channels, Intuition, and Epistemic Transformation in Higher Education

Abstract

AI as a cognitive partner whose knowledge disagreements can enhance learning and intellectual development rather than merely as a technological instrument. This research paper combines theories of dialogic pedagogy, cognitive dissonance, and epistemic humility via conceptual analysis. It looks at how Parallel Thinking Channels promote critical thinking, introspection, and the innovative combination of computational reasoning with human intuition. The purpose of this study is to advance the concept of dialogic cocreation by examining the revolutionary potential of generative AI in higher education portrays. This study utilized a quantitative approach with 419 undergraduate and post-graduate students as participants. The data analysis, conceptual model and hypotheses were evaluated employing Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS version 4.1.1.8 to analyse the associations between the constructs of the study. The results of study show that the disparities arising from the conflict between machine-generated results and human reasoning serve as triggers for transformative learning. By merging intuition with computational thinking, students enhance their ability to manage uncertainty, broaden their understanding, and cultivate flexible intellectual resilience. As a cooperative partner in knowledge development, this study offers a novel framework for incorporating generative AI into higher education. To prepare graduates for complex and uncertain digital futures, it emphasizes co-creation, critical reflection, and epistemic flexibility.

Keywords

• Dialogic Co-Creation
• Epistemic Transformative
• Higher education
• Generative AI
• Parallel Thinking Channels

References

1. An, Y., Yu, J. H., & James, S. (2025). Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 22(1), 10. https://doi.org/10.1186/s41239-025-00507-3
2. Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
3. Beale, R. (2025). Dialogic pedagogy for large language models: Aligning conversational AI with proven theories of learning. arXiv. https://doi.org/10.48550/arXiv.2506.19484
4. Brady, W. J., et al. (2025). Dual-process theory and decision-making in large language models. Nature Reviews Psychology. https://doi.org/10.1038/s44159-025-00506-1
5. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behaviour, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
6. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8
7. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). Sage.
8. Dann, C., Redmond, P., Fanshawe, M., Brown, A., Getenet, S., Shaik, T., Tao, X., Galligan, L., & Li, Y. (2024). Making sense of student feedback and engagement using artificial intelligence. Australasian Journal of Educational Technology, 40(3), 58–76. https://doi.org/10.14742/ajet.8903

9. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
10. Gilbert, S. J., Boldt, A., Sachdeva, C., Scarampi, C., & Tsai, P. C. (2023). Outsourcing memory to external tools: A review of “intention offloading.” Psychonomic Bulletin & Review, 30(1), 60–76. https://doi.org/10.3758/s13423-022-02139-4
11. Gronchi, G., & Perini, A. (2024). Dual-process theories of thought as potential architectures for developing neuro-symbolic AI models. Frontiers in Cognition, Volume 3. https://doi.org/10.3389/fcogn.2024.1356941
12. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modelling (PLS-SEM) (3rd ed.). Sage.
13. Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced issues in partial least squares structural equation modelling (PLS-SEM) (2nd ed.). Sage. ISBN: 9781071862506. https://doi.org/10.4135/9781071862520
14. Hwang, Y., & Lee, J. H. (2025). Exploring students’ experiences and perceptions of human-AI collaboration in digital content making. International Journal of Educational Technology in Higher Education, 22(1), 44. https://doi.org/10.1186/s41239-025-00542-0
15. Janssens, L., Kuppens, T., Andersson, P., & Struyven, K. (2024). Transformative learning in business economics education: The development of a measurement instrument to detect student perspectives. The International Journal of Management Education, 22(2), 100988. https://doi.org/10.1016/j.ijme.2024.100988
16. 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
17. Kalaitzidis, T. (2026). How generative AI fixes what higher education broke: reframing the “colonization” of knowing and learning. AI & Soc 41, 895–913 https://doi.org/10.1007/s00146-025-02629-8
18. Kim, N., Kang, S. W., Kim, S. J., & Park, H. J. (2023). Questioning unquestioned habits of mind: How executives learn new approaches to familiar situations through transformative learning. Academy of Management Learning & Education, 22(3), 481–506. https://doi.org/10.5465/amle.2021.0035
19. Küchemann, S., Avila, K. E., Dinc, Y., Hortmann, C., Revenga, N., & Kuhn, J. (2025). On opportunities and challenges of large multimodal foundation models in education. NPJ Science of Learning, 10(1), 11. https://doi.org/10.1038/s41539-025-00301-w
20. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), 56. https://doi.org/10.1186/s41239-023-00426-1
21. Leonardi, P. M. (2025). Homo agenticus in the age of agentic AI: Agency loops, power displacement, and the circulation of responsibility. Information and Organization, 35(3), 100582. https://doi.org/10.1016/j.infoandorg.2025.100582
22. Li, B., Bhattarai, A., & Ding, Z. (2025). In search of humanness: Professional identities of qualitative research educators in the age of generative AI. Learning, Media and Technology. https://doi.org/10.1080/17439884.2025.2521547
23. Liu, Z., Zuo, H., & Lu, Y. (2025). The impact of ChatGPT on students’ academic achievement: A meta-analysis. Journal of Computer Assisted Learning, 41(4), 70096. https://doi.org/10.1111/jcal.70096
24. Nic Giolla Mhichíl, M., Oliver, B., & Brown, M. (2023). A snapshot in time: The next new normal and micro-credentials. International Journal of Educational Technology in Higher Education, 20(1), 44. https://doi.org/10.1186/s41239-023-00414-7
25. Pennycook, G., & Rand, D. G. (2021). The psychology of fake news. Trends in Cognitive Sciences, 25(5), 388–402. https://doi.org/10.1016/j.tics.2021.02.007
26. Peña-García, P., Jaime-de-Aza, M., & Feltrero, R. (2026). Dialogic reflection and algorithmic bias: Pathways toward inclusive AI in education. Trends in Higher Education, 5(1), 9. https://doi.org/10.3390/higheredu5010009
27. Purwono, P., Ma’arif, A., Marhoon, H., Abougarair, A., & Sharkawy, A. (2025). Generative AI as a Pedagogical Co-Pilot in Higher Education: A Review of Adaptive Learning and Teacher Support Systems. Journal of Technological Pedagogy and Educational Development, 2(1), 28-42. doi:https://doi.org/10.59247/jtped.v2i1.8
28. Ramaul, L., Ritala, P., Kostis, A., & Aaltonen, P. (2025). Rethinking how we theorize AI in organization and management: A problematizing review of rationality and anthropomorphism. Journal of Management Studies. https://doi.org/10.1111/joms.13246
29. Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
30. Tang, P. M., Koopman, J., Mai, K. M., De Cremer, D., Zhang, J. H., Reynders, P., Ng, C. T. S., Chen, I.-H., & Chen, I. (2023a). No person is an island: Unpacking the work and after-work consequences of interacting with artificial intelligence. Journal of Applied Psychology, 108(11), 1766–1789. https://doi.org/10.1037/apl0001103
31. Tang, P. M., Koopman, J., Yam, K. C., De Cremer, D., Zhang, J. H., & Reynders, P. (2023b). The self‐regulatory consequences of dependence on intelligent machines at work: Evidence from field and experimental studies. Human Resource Management, 62(5), 721–744. https://doi.org/10.1002/hrm.22154
32. Ursavaş, O. F., Yalçın, Y., İslamoğlu, H., Bakır-Yalçın, E., & Cukurova, M. (2025). Rethinking the importance of social norms in generative AI adoption: Investigating the acceptance and use of generative AI among higher education students. International Journal of Educational Technology in Higher Education, 22(1), 38. https://doi.org/10.1186/s41239-025-00535-z
33. Wang, W., Gao, G., & Agarwal, R. (2024). Friend or foe? Teaming between artificial intelligence and workers with variation in experience. Management Science, 70(9), 5753–5775. https://doi.org/10.1287/mnsc.2023.4952
34. Wang, S., & Zhang, H. (2026). Pedagogical partnerships with generative AI in higher education: how dual cognitive pathways paradoxically enable transformative learning. Int J Educ Technol High Educ 23, 11 https://doi.org/10.1186/s41239-026-00585-x
35. Wegerif, R., & Casebourne, I. (2025). A dialogic theoretical foundation for integrating generative AI into pedagogical design. British Journal of Educational Technology, 57(3), 639–654. https://doi.org/10.1111/bjet.70026
36. Wei, X., Wang, L., Lee, L. K., & Liu, R. (2025). The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: An experimental study. International Journal of Educational Technology in Higher Education, 22(1), 23. https://doi.org/10.1186/s41239-025-00526-0
37. 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(1), 39. https://doi.org/10.1186/s41239-019-0171-0
38. Zeivots, S., & Shalavin, C. A. (2024). Codesigning meaningful engagement with online course readings: Implications for teaching management courses. Academy of Management Learning & Education, 23(2), 284–303. https://doi.org/10.5465/amle.2022.0182
39. Zhang, W., Zhang, W., Daim, T., & Yalçın, H. (2025). AI challenges conventional knowledge management: Light the way for reframing SECI model and Ba theory. Journal of Knowledge Management, 29(5), 1618–1654. https://doi.org/10.1108/JKM-02-2024-0203
40. Zhang, X., et al. (2025). Interactions with generative AI chatbots: Unveiling dialogic dynamics in university students’ creative problem-solving. Smart Learning Environments, 12(1). https://doi.org/10.1186/s41239-025-00508-2