YAPAY ZEKÂ İLE DÖNÜŞEN AKADEMİK KİMLİK: TEZ DÖNEMİNDEKİ BİR DOKTORA ÖĞRENCİSİNİN ANALİTİK OTOETNOGRAFİSİ

Abstract

Bu çalışma, doktora tez sürecindeki bir araştırmacının yapay zekâ (YZ) araçlarıyla yürüttüğü üretim süreçlerinin, akademik kimlik inşasını ve akademik özne olarak konumlanma biçimini nasıl dönüştürdüğünü anlamayı amaçlamaktadır. Kuramsal çerçeve, analitik otoetnografi yöntemine uygun biçimde; akademik öznellik, yaratıcı ortaklık, dijital etik duyarlılık ve bilişsel araç kuramı etrafında yapılandırılmıştır. Çalışmada, veriler araştırmacının YZ araçları eşliğinde oluşturduğu proje metinleri, makale ve bildiriler ile bu süreçlerde tuttuğu yansıtıcı günlüklerden elde edilmiştir. Veri analizi süreci üç aşamada gerçekleştirilmiştir: ön okuma ve kavramsal tarama, tematik kodlama ve kuramsal eksenlerle eşleme, yapılandırılmış yorumlama. Bulgular, YZ araçlarının yalnızca teknik bir kolaylaştırıcı değil; bilgiye eleştirel yaklaşımı teşvik eden, etik refleksiyonu artıran ve özgün yapı kurma becerilerini destekleyen düşünsel bir eşlikçi işlevi gördüğünü ortaya koymuştur. Sonuç olarak beş tema belirginleşmiştir: işlevsellik temelli yaklaşım, yapı kurma- yeniden inşa, etik süzgeç- içsel onay, akademik özgünlük ve kendilik, bütüncül akademik dönüşüm. Bu bulgular, YZ araçlarının akademik üretimde yalnızca araçsal değil; öznel ve etik bir ortak olarak işlev gördüğünü göstermektedir.

Keywords

• Yapay zekâ
• Analitik otoetnografi
• Akademik dönüşüm

ACADEMIC IDENTITY TRANSFORMED BY ARTIFICIAL INTELLIGENCE: AN ANALYTICAL AUTOETHNOGRAPHY OF A DOCTORAL STUDENT DURING THE DISSERTATION PROCESS

Abstract

This study aims to explore how the use of artificial intelligence (AI) tools during a doctoral dissertation process transforms a researcher’s academic identity construction and the modes of positioning as an academic subject. The theoretical framework was structured in line with the analytic autoethnography method and grounded in key concepts such as academic subjectivity, creative collabiration, digital ethical sensitivity, and the theory of cognitive tools. Data were collected through project texts, academic articles, and conference papers developed using AI tools, as well as reflective journals kept throughout these processes. The data analysis proceeded in three phases: preliminary reading and conceptual review, thematic coding and mapping aligned with theoretical axes, and structured interpretation. Findings reveal that AI tools functioned not merely as technical facilitators but as intellectual companions that supported critical engagement with information, strengthened ethical reflection, and fostered the abiliyt to construct original structures. Five major themes emerged: functionality-based approach, structuring–reconstruction, ethical filter–inner validation, academic originality and selfhood, and holistic academic transformation These results suggest that AI tools in academic production processes function not only as instrumental means but also as ethical and subjective collaborators that contribute meaningfully to the formation of scholarly agency.

Keywords

• Artificial intelligence
• Analytic autoethnography
• Academic transformation

References

1. Akkan, M. (2023). Eğitimin geleceği ve eğitimde yapay zekânın rolü. İzmir Şehir Koleji.
2. Anderson, L. (2006). Analytic autoethnography. Journal of Contemporary Ethnography, 35(4), 373–395. https://doi.org/10.1177/0891241605280449
3. Ball, S. J., & Grimaldi, E. (2022). Neoliberal education and the neoliberal digital classroom. Learning, Media and Technology, 47(2), 288–302. https://doi.org/10.1080/17439884.2021.1963980
4. Bebeau, M. J. (2002). The defining issues test and the four component model: Contributions to professional education. Journal of Moral Education, 31(3), 271–295. https://doi.org/10.1080/0305724022000008115
5. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
6. Büyükada, S. (2024). Akademik yazımda yapay zekâ kullanımının etik açıdan incelenmesi: ChatGPT örneği. Rize İlahiyat Dergisi, 26(26), 1–12. https://doi.org/10.32950/rid.1337208
7. Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187–1216. https://doi.org/10.1002/tea.20237
8. Chang, H. (2008). Autoethnography as method. Left Coast Press.

9. Charmaz, K. (2014). Constructing grounded theory: A practical guide through qualitative analysis. Sage Publications.
10. Ellis, C., Adams, T. E., & Bochner, A. P. (2011). Autoethnography: An overview. Historical Social Research, 36(4), 273–290.
11. Eriçok, B., Karataş, F., & Yüce, E. (2024). Öğretmen adaylarının yapay zekâya (YZ) ilişkin metafor algıları. Kocaeli University Journal of Education, 7, 607–630.
12. Ertmer, P. A., & Newby, T. J. (1996). The expert learner: Strategic, self-regulated, and reflective. Instructional Science, 24(1), 1–24. https://doi.org/10.1007/BF00156001
13. Gee, J. P. (2000). Identity as an analytic lens for research in education. Review of Research in Education, 25, 99–125. https://doi.org/10.3102/0091732x025001099
14. Günbey, M., Kara, E., & Eriçok, B. (2024). Eğitim kararlarını etkileyen etkenler: Bir sistematik derleme çalışması. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 57(1), 365–402. https://doi.org/10.30964/auebfd.1275386
15. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & 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
16. Işık, M., & Çamur, Ö. (2024). Yapay zekâ ve dijital okuryazarlık: Akademik çabada yeni dinamikler. Beykoz Akademi Dergisi, 12(2), 173–197. https://doi.org/10.14514/beykozad.1508294
17. Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
18. Kukulska-Hulme, A. (2021). Reflections on research questions in mobile assisted language learning. Journal of China Computer-Assisted Language Learning, 1(1), 28–46. https://doi.org/10.1515/jccall-2021-2002
19. Lin, Z., & Riedl, M. (2023). An ontology of co-creative AI systems. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (pp. 1–4).
20. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press. https://eric.ed.gov/?id=ED584840
21. Natarajan, S., Mathur, S., Sidheekh, S., Stammer, W., & Kersting, K. (2024). Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? http://arxiv.org/abs/2412.14232
22. Pea, R. D. (1985). Beyond Amplification: Using the Computer to Reorganize Mental Functioning. Educational Psychologist, 20(4), 167–182. https://doi.org/10.1207/s15326985ep2004_2
23. Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–115). Cambridge University Press.
24. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
25. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
26. UNESCO. (2024). Eğitim ve araştırmada üretken yapay zekâ kılavuzu (I. B. Ergül, Trans.; A. L. Kaban, Ed.). Birleşmiş Milletler Eğitim, Bilim ve Kültür Örgütü. https://www.unesco.org/en/open-access/cc-sa
27. UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
28. Watermeyer, R., Phipps, L., Lanclos, D., & Knight, C. (2024). Generative AI and the Automating of Academia. Postdigital Science and Education, 6(2), 446–466. https://doi.org/10.1007/s42438-023-00440-6
29. Weller, M. (2020). 25 years of ed tech. https://doi.org/10.15215/aupress/9781771993050.01
30. World Economic Forum. (2025). Future of jobs report 2025. https://www.weforum.org/reports/the-future-of-jobs-report-2025/