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

Year 2025, Volume: 27 Issue: 2, 278 - 293

Derya Orhan Göksün

https://doi.org/10.17556/erziefd.1648080

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

References

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

Year 2025, Volume: 27 Issue: 2, 278 - 293

Derya Orhan Göksün

https://doi.org/10.17556/erziefd.1648080

Abstract

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.

Keywords

Yapay zekâ üst düzey soru oluşturma SWOT öğretmen eğitimi

Ethical Statement

Bu çalışma Adıyaman Üniversitesi Sosyal ve Beşerî Bilimler Etik Kurulu tarafından 5.11.2024 tarih 135 karar sayısı ile oy birliği ile verilen etik kurul kararı sonucunda yürütülmüştür. Katılımcılar bilgilendirilmiş ve tamamen gönüllük esası ile çalışmaya katılmaları sağlanmıştır. Araştırmanın verileri yalnızca araştırmacı tarafından saklanmaktadır ve üçüncü herhangi bir kişi ile paylaşılmamıştır.

Supporting Institution

Çalışmada hiçbir kurum veya kuruluştan destek alınmamıştır.

Thanks

Çalışmada teşekkür edilmesi gereken bir kurum, kuruluş ya da kişi bulunmamaktadır.

References

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  • 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
  • Bogdan, R. C. ve Biklen, S. K. (2003). Qualitative research for education: An introduction to theories and methods (4. baskı). Pearson Education.
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  • Bozkurt, A. (2023). ChatGPT, üretken yapay zeka ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72. https://doi.org/10.59320/alanyazin.1283282
  • 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
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  • 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
  • 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
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  • 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).
  • Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş. ve Demirel, F. (2018). Bilimsel araştırma yöntemleri (24. baskı). Pegem Akademi: Ankara.
  • 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
  • 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
  • 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.
  • 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
  • Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4. baskı). Pearson Education.
  • Creswell, J. W. (2014). Research design: Qualitative, quantitative and mixed methods approaches (4. baskı). Thousand Oaks, CA: Sage.
  • 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
  • Euchner, J. (2023). Generative AI. Research-Technology Management, 66(3), 71-74. https://doi.org/10.1080/08956308.2023.2188861
  • 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
  • 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
  • Foster, D. (2022). Generative deep learning. O'Reilly Media, Inc.
  • 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
  • Güneş, F. (2012). Öğrencilerin düşünme becerilerini geliştirme. Türklük Bilimi Araştırmaları, 32, 127-146.
  • 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
  • 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
  • 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
  • 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
  • Jo, A. (2023). The promise and peril of generative AI. Nature, 614(1), 214-216.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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).
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There are 53 citations in total.

Details

Primary Language Turkish
Subjects Educational Technology and Computing
Journal Section In This Issue
Authors

Derya Orhan Göksün 0000-0003-0194-0451

Early Pub Date June 19, 2025
Publication Date
Submission Date February 27, 2025
Acceptance Date May 22, 2025
Published in Issue Year 2025 Volume: 27 Issue: 2

Cite

APA Orhan Göksün, D. (2025). Öğretmen Adaylarının Üst Düzey Düşünme Becerilerini Geliştirecek Soru Etkinliklerinde Yapay Zekâ Araçlarının Kullanımına Yönelik SWOT Analizleri. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 27(2), 278-293. https://doi.org/10.17556/erziefd.1648080

Erzincan University Journal of Education Faculty: An International Journal of Educational Research