Öğretmen Adaylarının Üst Düzey Düşünme Becerilerini Geliştirecek Soru Etkinliklerinde Yapay Zekâ Araçlarının Kullanımına Yönelik SWOT Analizleri

Öz

Bu çalışmada, öğretmen adaylarının üst düzey düşünme becerilerini geliştirecek soru etkinliklerinde yapay zekâ araçlarının kullanımına yönelik SWOT analizi ele alınmaktadır. Araştırmada, öğretmen adaylarının üretken yapay zekâ araçları ile soru üretme süreçleri ve bu süreçlere yönelik görüşleri incelenmektedir. Eğitimde yapay zekâ araçlarının kullanımına yönelik ders almış 58 öğretmen adayı çalışmaya katılmıştır. Nitel araştırma yaklaşımı çerçevesinde desenlenen bu araştırmada alınan görüşler tümevarımsal analiz ve SWOT ile analiz edilmiştir. Analizler önce uzman görüşleri ile sonra katılımcı onayları ile doğrulanmıştır. Çalışmada, öğretmen adaylarının yapay zekâ araçları ile oluşturdukları soruların nitelikleri, kullanım kolaylığı, ekonomikliği, verimliliği gibi olumlu bağlamda; etik kaygılar, kısıtlamalar, tekrara düşme gibi olumsuz bağlamda görüşler bildirdikleri görülmüştür. Ayrıca, yapay zekâ araçlarının güçlü ve zayıf yönleri, sundukları fırsatlar ve oluşturdukları tehditler analiz edilmiştir. Araştırma sonuçları, yapay zekâ araçlarının öğretim süreçlerinde etkin bir şekilde kullanılabileceğini, ancak etik ve güvenilirlik konularında dikkatli olunması gerektiğini göstermektedir. Öğretmen adaylarının yapay zekâ araçları ile soru üretme süreçlerinde karşılaştıkları zorluklar ve bu araçların eğitimdeki potansiyel faydaları detaylı bir şekilde tartışılmıştır.

Anahtar Kelimeler

• Yapay zekâ
• üst düzey soru oluşturma
• SWOT
• öğretmen eğitimi

SWOT Analyses for the Use of Artificial Intelligence Tools in Question Activities to Develop Higher Order Thinking Skills of Pre-Service Teachers

Abstract

This study deals with pre-service teachers' SWOT analyses on the use of artificial intelligence tools in question activities that will develop their students’ higher-order thinking skills. The study examines pre-service teachers' processes of generating questions with generative artificial intelligence tools and their views on these processes. Fifty-eight pre-service teachers who have taken a course on the use of artificial intelligence tools in education participated in the study. The opinions received in this study, which was designed within the framework of a qualitative research approach, were evaluated by inductive analysis and SWOT analysis. The analyses were verified first with expert opinions and then with participant approvals. In the study, it was observed that pre-service teachers expressed opinions in positive contexts such as the qualities of the questions they created with artificial intelligence tools, ease of use, economy, efficiency, and in negative contexts such as ethical concerns, limitations, and repetition. In addition, the strengths and weaknesses of artificial intelligence tools, the opportunities they offer and the threats they pose were analyzed. The results of the research show that artificial intelligence tools can be used effectively in teaching processes, but care should be taken in terms of ethics and reliability.

Keywords

• Artificial intelligence
• creating meta-cognitive questions
• SWOT
• teacher education

Kaynakça

1. Amaro, I., Barra, P., Della Greca, A., Francese, R. ve Tucci, C. (2024). Believe in artificial intelligence? A user study on the ChatGPT’s fake information impact. IEEE Transactions on Computational Social Systems, 11(4), 5168-5177. 10.1109/TCSS.2023.3291539
2. Arslan, K. (2020). Eğitimde yapay zeka ve uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71-88.
3. Baidoo-Anu, D. ve Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
4. Bogdan, R. C. ve Biklen, S. K. (2003). Qualitative research for education: An introduction to theories and methods (4. baskı). Pearson Education.
5. Borge, M., Smith, B. K. ve Aldemir, T. (2024). Using generative ai as a simulation to support higher-order thinking. International Journal of Computer-Supported Collaborative Learning, 19(4), 479-532. https://doi.org/10.1007/s11412-024-09437-0
6. Bozkurt, A. (2023). ChatGPT, üretken yapay zeka ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72. https://doi.org/10.59320/alanyazin.1283282
7. Bozkurt, A. (2023b). Unleashing the potential of generative AI, conversational agents and chatbots in educational praxis: A systematic review and bibliometric analysis of GenAI in education. Open Praxis, 15(4), 261-270. https://doi.org/10.55982/openpraxis.15.4.609
8. Bozkurt, Ş. B. ve Çakır, H. (2016). Ortaokul öğrencilerinin 21. yüzyıl öğrenme beceri düzeylerinin cinsiyet ve sınıf seviyesine göre incelenmesi. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 39(39), 69-82. https://dx.doi.org/10.9779/PUJE757

9. Breithaupt, F., Otenen, E., Wright, D. R., Kruschke, J. K., Li, Y. ve Tan, Y. (2024). Humans create more novelty than ChatGPT when asked to retell a story. Scientific Reports, 14(1), 875. https://doi.org/10.1038/s41598-023-50229-7
10. Briganti, G. (2024). How ChatGPT works: A mini review. European Archives of Oto-Rhino-Laryngology, 281(3), 1565-1569. https://doi.org/10.1007/s00405-023-08337-7
11. Brown, M. T. ve Farris, J. S. (2017). Toward an understanding of the AI productivity paradox: A conceptual framework and research agenda. Information & Management, 54(7), 826-837.
12. Büyükgöze, S. ve Dereli, E. (2019). Dijital sağlık uygulamalarında yapay zeka. In VI. Uluslararası Bilimsel ve Mesleki Çalışmalar Kongresi-Fen ve Sağlık (Vol. 7, No. 10).
13. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2018). Bilimsel araştırma yöntemleri (24. baskı). Pegem Akademi: Ankara.
14. Cano-Marin, E. (2024). The transformative potential of Generative Artificial Intelligence (GenAI) in business: a text mining analysis on innovation data sources. ESIC Market, 55(2), e333-e333. https://doi.org/10.7200/esicm.55.333
15. Chang, H. H. ve Huang, W. C. (2006). Application of a quantification SWOT analytical method. Mathematical and Computer Modelling, 43(1-2), 158-169. https://doi.org/10.1016/j.mcm.2005.08.016
16. Chenneville, T., Duncan, B. ve Silva, G. (2024). More questions than answers: Ethical considerations at the intersection of psychology and generative artificial intelligence. Translational Issues in Psychological Science, 10(2), 162-178.
17. Chiu, T. K. (2023). The impact of generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 1-17. https://doi.org/10.1080/10494820.2023.2241510
18. Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4. baskı). Pearson Education.
19. Creswell, J. W. (2014). Research design: Qualitative, quantitative and mixed methods approaches (4. baskı). Thousand Oaks, CA: Sage.
20. Dyson, R. G. (2004). Strategic development and SWOT analysis at the University of Warwick. European Journal of Operational Research, 152(3), 631-640. https://doi.org/10.1016/S0377-2217(03)00062-6
21. Euchner, J. (2023). Generative AI. Research-Technology Management, 66(3), 71-74. https://doi.org/10.1080/08956308.2023.2188861
22. Farrokhnia, M., Banihashem, S. K., Noroozi, O. ve Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
23. Floridi, L. ve Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30, 681-694. https://doi.org/10.1007/s11023-020-09548-1
24. Foster, D. (2022). Generative deep learning. O'Reilly Media, Inc.
25. Giray, L. (2023). Prompt engineering with ChatGPT: A guide for academic writers. Annals of Biomedical Engineering, 51(12), 2629-2633. https://doi.org/10.1007/s10439-023-03272-4
26. Güneş, F. (2012). Öğrencilerin düşünme becerilerini geliştirme. Türklük Bilimi Araştırmaları, 32, 127-146.
27. Hanushek, E. A. (2005). The economics of school quality. German Economic Review, 6(3), 269-286. https://doi.org/10.1111/j.1468-0475.2005.00132.x
28. Hanushek, E. A. ve Woessmann, L. (2011). The economics of international differences in educational achievement. Handbook of the Economics of Education, 3, 89-200. https://doi.org/10.1016/B978-0-444-53429-3.00002-8
29. Hsiao, C. H. ve Tang, K. Y. (2024). Beyond acceptance: an empirical investigation of technological, ethical, social, and individual determinants of GenAI-supported learning in higher education. Education and Information Technologies, 1-26. https://doi.org/10.1007/s10639-024-13263-0
30. Hurlburt, G. (2023). What if ethics got in the way of generative AI? IT Professional, 25(2), 4-6. https://www.doi.org/10.1109/MITP.2023.3267140
31. Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216.
32. Krathwohl, D. R. (2002). A revision of Bloom's taxonomy: An overview. Theory into practice, 41(4), 212-218. https://doi.org/10.1207/s15430421tip4104_2
33. Kumar, A., Dabas, V. ve Hooda, P. (2018). Text classification algorithms for mining unstructured data: A SWOT analysis. International Journal of Information Technology, 1-11. https://doi.org/10.1007/s41870-018-0213-y
34. Kurttila, M., Pesonen, M., Kangas, J. ve Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest Policy and Economics, 1(1), 41-52. https://doi.org/10.1016/S1389-9341(99)00004-0
35. Laine, J., Minkkinen, M. ve Mäntymäki, M. (2025). Understanding the Ethics of Generative AI: Established and New Ethical Principles. Communications of the Association for Information Systems, 56(1), 7. https://www.doi.org/10.17705/1CAIS.05601
36. Levine, S., Beck, S. W., Mah, C., Phalen, L. ve PIttman, J. (2025). How do students use ChatGPT as a writing support?. Journal of Adolescent & Adult Literacy, 68(5), 445-457. https://doi.org/10.1002/jaal.1373
37. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H. ve Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, 55(9), 1-35. https://doi.org/10.1145/3560815
38. Muller, M., Weisz, J. D. ve Geyer, W. (2020). Mixed initiative generative AI interfaces: An analytic framework for generative AI applications. In Proceedings of the Workshop The Future of Co-Creative Systems-A Workshop on Human-Computer Co-Creativity of the 11th International Conference on Computational Creativity (ICCC 2020).
39. Orhan Göksün, D. ve Kurt, A. A. (2017). Öğretmen adaylarının 21. yy. öğrenen becerileri kullanımları ve 21. yy. öğreten becerileri kullanımları arasındaki ilişki. Eğitim ve Bilim, 42(190). https://dx.doi.org/10.15390/EB.2017.7089
40. Peres, R., Schreier, M., Schweidel, D. ve Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.02.004
41. Pickton, D. W. ve Wright, S. (1998). What’s SWOT in strategic analysis? Strategic Change, 7(2), 101-109. https://doi.org/10.1002/(SICI)1099-1697(199803/04)7:2<101::AID-JSC332>3.0.CO;2-6
42. Pirim, H. (2006). Yapay zeka. Journal of Yaşar University, 1(1), 81-93. https://doi.org/10.19168/jyu.72783 Prokopenko, J. (1992). Verimlilik Yönetimi. Uygulamalı El Kitabı. Çev: Olcay Baykal. Mill i Prodüktivite Merkezi Yayınları: Ankara.
43. Qamariyah, S. N., Rahayu, S., Fajaroh, F. ve Alsulami, N. M. (2021). The Effect of Implementation of Inquiry-Based Learning with Socio-Scientific Issues on Students' Higher-Order Thinking Skills. Journal of Science Learning, 4(3), 210-218. DOI: 10.17509/jsl.v4i3.30863
44. Rodrigues, L., Pereira, F. D., Cabral, L., Gašević, D., Ramalho, G. ve Mello, R. F. (2024). Assessing the quality of automatic-generated short answers using GPT-4. Computers and Education: Artificial Intelligence, 7, 100248. https://doi.org/10.1016/j.caeai.2024.100248
45. Sætra, H. S. (2023). Generative AI: Here to stay, but for good? Technology in Society, 75, 102372. https://doi.org/10.1016/j.techsoc.2023.102372
46. Shukla, R., Mishra, A. K., Banerjee, N. ve Verma, A. (2024). The comparison of ChatGPT 3.5, Microsoft Bing, and Google Gemini for diagnosing cases of neuro-ophthalmology. Cureus, 16(4). https://www.doi.org/10.7759/cureus.58232
47. Su, J. ve Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education. https://doi.org/10.1177/20965311231168423
48. Tassoti, S. (2024). Assessment of students use of generative artificial intelligence: Prompting strategies and prompt engineering in chemistry education. Journal of Chemical Education, 101(6), 2475-2482. https://doi.org/10.1021/acs.jchemed.4c00212
49. Ültay, E. ve Uludüz, Ş. M. (2016). Fen bilimleri dersi kapsamında teknoloji uygulamaları ve tasarımına ilişkin öğretmen görüşleri. Bartın University Journal of Faculty of Education, 5(2), 512-535. https://doi.org/10.14686/buefad.v5i2.5000174097
50. van der Zant, T., Kouw, M. ve Schomaker, L. (2013). Generative artificial intelligence. Springer Berlin Heidelberg. Wang, S. ve Jin, P. (2023). A brief summary of prompting in using GPT models. Qeios, no. April. https://doi.org/10.32388/IMZI2Q
51. Yeadon, W., Peach, A. ve Testrow, C. (2024). A comparison of human, GPT-3.5, and GPT-4 performance in a university-level coding course. Scientific Reports, 14(1), 23285. https://doi.org/10.1038/s41598-024-73634-y
52. Yin, R.K. (2012). Applications of case study research (3. Baskı). Thousand Oaks, Ca: Sage.
53. Zlotnikova, I., Hlomani, H., Mokgetse, T. ve Bagai, K. (2025). Establishing ethical standards for GenAI in university education: a roadmap for academic integrity and fairness. Journal of Information, Communication and Ethics in Society.