Araştırma Yöntemleri Eğitiminde Yapay Zekâ ile Akademik Metin Analizi: Tutum ve Deneyimler

Öz

Günümüzde YZ, eğitim ortamlarında giderek daha fazla kullanılmakta ve bilimsel araştırma süreçlerini destekleyerek öğrencilere kişiselleştirilmiş öğrenme deneyimleri sunmaktadır. Bu doğrultuda bu çalışma, araştırma yöntem ve teknikleri dersinde yapay zekâ kullanımının öğrencilerin derse yönelik tutumlarına etkisini incelemeyi amaçlamaktadır. Araştırmada açıklayıcı sıralı karma yöntem kullanılmıştır. Çalışma grubu, 2024/2025 akademik yılında bir üniversitede araştırma yöntem ve teknikleri dersini alan 24 öğrenciden oluşmaktadır. Veri toplama sürecinde nicel veriler için tutum ölçeği, nitel veriler için ise odak grup görüşmesi kullanılmıştır. Analiz sonuçlarına göre, yapay zekâ kullanımının öğrencilerin derse yönelik genel tutumlarını olumlu yönde etkilediği bulunmuştur. Özellikle bilişsel özgüven ve ilgi boyutlarında anlamlı bir artış gözlemlenirken, dersin önemine ve mesleki yaşantıya uyarlanabilirliğine ilişkin algıda anlamlı bir değişim olmamıştır. Ayrıca, öğrenciler yapay zekânın araştırma süreçlerini hızlandırdığını, hata payını azalttığını ve akademik yazım sürecini desteklediğini belirtmişlerdir. Ancak, bazı öğrenciler yapay zekâ kullanımının bilginin akılda kalıcılığına olumsuz etkisi olabileceğini ve derse katılımı azaltabileceğini ifade etmiştir. Sonuç olarak, YZ destekli eğitim ortamlarının öğrenci motivasyonu ve bilişsel özgüven üzerinde olumlu etkileri olduğu görülmektedir. Ancak, bağımsız öğrenme becerilerini güçlendirmek, eleştirel düşünmeyi teşvik etmek ve dersin mesleki bağlamla daha fazla ilişkilendirilmesini sağlamak için ek stratejilere ihtiyaç duyulmaktadır.

Anahtar Kelimeler

• yapay zeka
• araştırma yöntemleri eğitimi
• öğrenci tutumu
• öğrenci görüşleri
• eğitim teknolojileri

Etik Beyan

Bu araştırma, Amasya Üniversitesi Sosyal Bilimler Etik Kurulu’nun 14/04/2025 tarihli E-30640013-108.01-255232 sayılı kararı ile alınan izinle yürütülmüştür.

Academic Text Analysis with Artificial Intelligence in Research Methods Education: Attitudes and Experiences

Öz

Today, AI is increasingly used in educational environments, supporting scientific research processes and providing students with personalized learning experiences. In line with this, the study seeks to investigate the influence of AI use in a scientific research methods course on students' attitudes toward the course. An explanatory sequential mixed methods design was employed in the study. The study group consisted of 24 students taking a scientific research methods course at a university in the 2024-2025 academic year. An attitude scale was used for quantitative data collection, while focus group interviews were used for qualitative data. The analysis results indicated that AI use positively impacted students' overall attitudes toward the course. While significant increases were observed in cognitive self-confidence and interest, there was no significant change in the perception of the course's importance or its adaptability to professional life. Furthermore, students stated that AI accelerated research processes, reduced the margin of error, and supported academic writing. However, some students expressed that AI use could negatively impact knowledge retention and reduce course participation. In conclusion, AI-supported learning environments appear to have positive effects on student motivation and cognitive self-confidence. However, additional strategies are needed to strengthen independent learning skills, encourage critical thinking, and further connect the course to the professional context.

Anahtar Kelimeler

• Artificial Intelligence
• Research Methods Education
• Student Attitude
• Student Perspectives
• Educational Technologies

Etik Beyan

This research was conducted with the permission obtained by the decision of Amasya University Social Sciences Ethics Committee dated 14/04/2025 and numbered E-30640013-108.01-255232.

Kaynakça

1. Verdiğiniz kaynakçayı her bir referans arasında birer satır boşluk (ENTER) olacak şekilde düzenledim:
2. Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7
3. Akinwalere, S. N., & Ivanov, V. T. (2022). Artificial intelligence in higher education: Challenges and opportunities. Border Crossing, 12(1), 1–15. https://doi.org/10.33182/bc.v12i1.2015
4. Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965-2971. https://doi.org/10.1021/acs.jchemed.3c00323
5. Aleven, V., McLaren, B. M., Sewall, J., van Velsen, M., Popescu, O., Demi, S., Ringenberg, M., & Koedinger, K. R. (2016). Example-tracing tutors: Intelligent tutor development for non-programmers. International Journal of Artificial Intelligence in Education, 26(1), 224–269. https://doi.org/10.1007/s40593-015-0088-2
6. Arslan, K. (2020). Eğitimde yapay zekâ ve uygulamaları. Batı Anadolu Eğitim Bilimleri Dergisi, 11(1), 71-88.
7. Bai, L., Liu, X., & Su, J. (2023). ChatGPT: The cognitive effects on learning and memory [Perspective]. Brain‑X, 1(3), e30. https://doi.org/10.1002/brx2.30
8. Baidoo-Anu, D., & Owusu Ansah, L. (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

9. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Lárusson & B. White (Eds.), Learning analytics (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
10. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman/Times Books/ Henry Holt & Co.
11. Başaran, R., & Yeşilbaş Özenç, Y. (2024). Bilimsel araştırma sürecinde yapay zekâ araçlarının kullanımı. Uluslararası Eğitimde Mükemmellik Arayışı Dergisi (UEMAD), 4(1), 35-53.
12. Bhaskar, S., & Manjuladevi, M. (2016). Methodology for research II. Indian Journal of Anaesthesia, 60, 646 - 651. https://doi.org/10.4103/0019-5049.190620
13. Biggs, J., & Tang, C. (2011). Teaching for quality learning at university. McGraw-Hill Education.
14. Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives: The classification of educational goals, Handbook I: Cognitive domain. David McKay.
15. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189X018001032
16. Büyükada, S. (2024). Akademik yazımda yapay zekâ kullanımının etik açıdan incelenmesi: ChatGPT örneği. Rize İlahiyat Dergisi, 26, 1-12. https://doi.org/10.32950/rid.1337208
17. Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002. https://doi.org/10.1103/RevModPhys.91.045002
18. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
19. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches (2nd ed.). Sage.
20. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry & research design: Choosing among five approaches (4th ed.). Sage Publications.
21. D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
22. Dewey, J. (1938). Experience and education. Macmillan.
23. Eraut, M. (2000). Non‐formal learning and tacit knowledge in professional work. British Journal of Educational Psychology, 70(1), 113-136. https://doi.org/10.1348/000709900158001
24. Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. The American Journal of Distance Education, 19(3), 133-148. https://doi.org/10.1207/s15389286ajde1903_2
25. Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486-489. https://doi.org/10.5812/ijem.3505
26. Gilbert, N. (1976). The transformation of research findings into scientific knowledge. Social Studies of Science, 6, 281 - 306. https://doi.org/10.1177/030631277600600302
27. Gönültaş, Y. C. (2025). Yapay zekâ ve bilimsel metin yazımı: Türk kamu yönetimi alanyazınında ChatGPT4.0 örneği. Uluslararası Yönetim Akademisi Dergisi, 7(3), 827-843. https://doi.org/10.33712/mana.1578165
28. Guest, G., Namey, E., & McKenna, K. (2017). How many focus groups are enough? Building an evidence base for nonprobability sample sizes. Field Methods, 29(1), 3–22. https://doi.org/10.1177/1525822X16639015
29. Gupta, A., Carpenter, D., Min, W., Rowe, J., Azevedo, R., & Lester, J. (2024). Detecting and mitigating encoded bias in deep learning-based stealth assessment models for reflection-enriched game-based learning environments. International Journal of Artificial Intelligence in Education, 34(3), 1138-1165. https://doi.org/10.1007/s40593-023-00379-6
30. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
31. Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 324–333. https://doi.org/10.36941/ajis-2021-0077
32. Jiang, Y. (2024). Research on the Application of Intelligent Tutoring System in Personalized English Teaching in Colleges. In Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Education (pp. 57-61). https://doi.org/10.1145/3722237.3722248
33. Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.
34. Kox, E. S., & Beretta, B. (2024). Evaluating generative AI incidents: An exploratory vignette study on the role of trust, attitude and AI literacy. In F. Lorig, J. Tucker, A. Dahlgren Lindstrom, F. Dignum, P. Murukannaiah, A. Theodorou, & P. Yolum (Eds.), Hybrid Human-AI systems for the social good: Proceedings of the 3rd International Conference on Hybrid Human-Artificial Intelligence (HHAI 2024) (pp. 188–198). IOS Press. https://doi.org/10.3233/FAIA240194
35. Krasheninnik, I. V., Chorna, A. V., Koniukhov, S. L., Ibrahimova, L., & Serdiuk, I. (2022). Artificial Intelligence in Personalized ICT Learning. IJCSNS International Journal of Computer Science and Network Security, 22(2), 159-166. https://doi.org/10.22937/IJCSNS.2022.22.2.21
36. Latour, B. (1998). Essays on science and society: From the world of science to the world of research. Science, 280(5361), 208-209. https://doi.org/10.1126/science.280.5361.208
37. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. https://doi.org/10.1017/CBO9780511815355
38. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
39. Maphosa, V., & Maphosa, M. (2023). Artificial intelligence in higher education: a bibliometric analysis and topic modeling approach. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2261730
40. Mayer, R. E. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
41. Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policymakers. UNESCO. https://doi.org/10.54675/PCSP7350
42. Nayıroğlu, B., & Tutak, T. (2024). Matematik öğretiminde yapay zekânın rolü: Eğitimde kullanılan araçların incelenmesi. Turkish Journal of Educational Studies, 11(1), 65-78. https://doi.org/10.33907/turkjes.1415591
43. Nguyen, A., Ngo, H., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
44. Nilsson, N. J. (2010). The quest for artificial intelligence. Cambridge University Press.
45. O’Cathain, A., Murphy, E., & Nicholl, J. (2010). Three techniques for integrating data in mixed methods studies. BMJ, 341, c4587. https://doi.org/10.1136/bmj.c4587
46. Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development (Working Papers on Education Policy No. 7). UNESCO. https://www.unesco.org/en/digital-education/artificial-intelligence
47. Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications. Prentice Hall.
48. Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
49. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
50. Selwyn, N. (2020). Telling tales on technology: Qualitative studies of technology and education. Routledge. https://doi.org/10.4324/9781138368736
51. Vaid, S., Puntoni, S., & Khodr, A. (2023). Artificial intelligence and empirical consumer research: A topic modeling analysis. Journal of Business Research, 166, 114110. https://doi.org/10.1016/j.jbusres.2023.114110
52. Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66–84. https://doi.org/10.1609/aimag.v34i4.2490
53. Yaşar, M. (2014). Developing an attitude scale related to scientific research methods course: Validity and reliability. Journal of Educational Sciences Research, 4(2), 109-129.
54. Yıldırım, A., & Şimşek, H. (2016). Sosyal bilimlerde nitel araştırma yöntemleri (10. Baskı). Seçkin Yayıncılık.
55. 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(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0
56. Zhang, F., Wang, X. Y., & Zhang, X. H. (2025). Applications of deep learning method of artificial intelligence in education. Education and Information Technologies, 30(2), 1563–1587. https://doi.org/10.1007/s10639-024-12883-w
57. Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016