ERCİYES ÜNİVERSİTESİ SPOR BİLİMLERİ FAKÜLTESİ ÖĞRENCİLERİNİN YAPAY ZEKA OKURYAZARLIK DÜZEYLERİNİN BİLİŞİMSEL DÜŞÜNME BECERİLERİ İLE İLİŞKİSİNİN İNCELENMESİ

Abstract

Amaç: Bu çalışma, Spor Bilimleri Fakültesi öğrencilerinin yapay zekâ okuryazarlığı ile bilişimsel düşünme becerileri arasındaki ilişkiyi incelemeyi, bu değişkenlerin demografik özelliklere göre farklılaşıp farklılaşmadığını belirlemeyi ve söz konusu yapılar arasındaki ilişkinin yönünü ortaya koymayı amaçlamaktadır. Yöntem: Araştırmada ilişkisel tarama modeli kullanılmıştır. Çalışma grubunu 400 gönüllü öğrenci oluşturmaktadır. Veriler, Yapay Zekâ Okuryazarlığı Ölçeği ve Bilişimsel Düşünme Ölçeği kullanılarak toplanmıştır. Veri analizinde betimsel istatistikler, bağımsız örneklemler t-testi ve Pearson korelasyon analizi uygulanmıştır. Bulgular: Elde edilen bulgular, hem bilişimsel düşünme hem de yapay zekâ okuryazarlığı düzeylerinin orta düzeyin üzerinde olduğunu göstermektedir. Sınıf düzeyine göre yapılan karşılaştırmalarda, dördüncü sınıf öğrencileri lehine anlamlı farklılıklar tespit edilmiştir. Cinsiyete göre farklılıklar yalnızca yapay zekâ okuryazarlığının farkındalık alt boyutunda erkekler lehine bulunmuştur. Bilgisayara sahip olan ve teknolojiyi aktif olarak takip eden öğrencilerin bazı alt boyutlarda daha yüksek puanlara sahip olduğu belirlenmiştir. Ayrıca, bilişimsel düşünme ile yapay zekâ okuryazarlığı arasında pozitif yönde ve anlamlı bir ilişki olduğu saptanmıştır (r = .489, p < .001). Sonuç: Elde edilen sonuçlar, bilişimsel düşünme becerilerinin yapay zekâ okuryazarlığı ile pozitif yönde ilişkili olduğunu ve spor bilimleri öğrencilerinin dijital yeterliklerinin geliştirilmesinde önemli bir rol oynayabileceğini göstermektedir.

Keywords

• Spor
• Bilişimsel Düşünme
• Artificial Intelligence Literacy

An Examination of the Relationship Between Artificial Intelligence Literacy Levels and Computational Thinking Skills of Students at Erciyes University Faculty of Sport Sciences

Abstract

Purpose: This study aims to examine the relationship between artificial intelligence lite-racy and computational thinking skills among students of the Faculty of Sport Sciences, to determine differences according to demographic variables, and to identify the direction of the relationship between these constructs. Methods: A relational research design was employed. The sample consisted of 400 volunteer students. Data were collected using the Artificial Intelligence Literacy Scale and the Computational Thinking Scale. Descriptive statistics, independent samples t-tests, and Pearson correlation analysis were conducted. Results: Findings showed that both computational thinking and AI literacy levels were above moderate. Significant differences were found in favor of fourth-year students. Gen-der differences were observed only in the awareness dimension of AI literacy in favor of males. Students with computers and those actively following technology had higher sco-res in several subdimensions. A significant positive relationship was found between computational thinking and AI literacy (r = .489, p < .001). Conclusions: The results indi-cate that computational thinking skills are positively associated with AI literacy and may play an important role in enhancing digital competencies among sport sciences students.

Keywords

• Sport
• Computational Thinking Skills
• Artificial Intelligence Literacy

References

1. Aho, A. V. (2012). Computation and computational thinking. The computer journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
2. Aşçı, H., Doğu, G., Yaman, H., Mirzeoğlu, N., Çelebi, M., Kirazcı, S., et al. (2011). Introduction to sports sciences. In N. Mirzeoğlu (Ed.), Introduction to sports sciences (pp. 7–285). Ankara: Spor Publishing.
3. Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661-670. https://doi.org/10.1016/j.robot.2015.10.008
4. Balcıoğlu, İ. (2003). Sociology and psychology of sport. Istanbul: Bilge Publishing.
5. Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2024). Scientific research methods (36th ed.). Ankara: Pegem Academy.
6. Christensen, L. B., Johnson, R. B., & Turner, L. A. (2010). Research methods, design, and analysis (11th ed.). Allyn & Bacon.
7. Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291–306. https://doi.org/10.52911/itall.1401740
8. Çınar, V., Aktop, A., Karahüseyinoğlu, F. M., Deniz, M., Savucu, Y., Demir, E., et al. (2015). Introduction to sports sciences. In E. Demir (Ed.), Introduction to sports sciences (pp. 3–349). Ankara: Nobel Academic Publishing.

9. Denning, P. J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28-30.
10. Dolmacı, A., & Akhan, N. E. (2020). Bilişimsel Düşünme Becerileri Ölçeğinin Geliştirilmesi: Geçerlik ve Güvenirlik Çalışması. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 9(3), 3050-3071. https://doi.org/10.15869/itobiad.698736
11. Futschek, G. (2006). Algorithmic thinking. In R. T. Mittermeir (Ed.), Informatics education (pp. 159–168). Berlin: Springer.
12. Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in human behavior, 72, 678-691. https://doi.org/10.1016/j.chb.2016.08.047
13. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California management review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
14. Halpern, D. F. (2014). Thought and knowledge (pp. 1–300). New York: Psychology Press.
15. Hermann, E. (2022). Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective. New media & society, 24(5), 1258-1277. https://doi.org/10.1177/14614448211022702
16. Ismail, R., Steinbach, T. A., & Miller, C. S. (2022, March). A guide towards a definition of computational thinking in k-12. In 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 801-810). IEEE.
17. Karasar, N. (2024). Scientific research method: Concepts, principles, and techniques (39th ed.). Ankara: Nobel Academic Publishing.
18. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
19. Kuleli, S. Ç. (2018). Evaluation of preservice teachers’ readiness for online learning and their computational thinking skills [Unpublished Master’s thesis]. Düzce University.
20. Kumar, R., Bogia, P., Haq, A. U., & Singh, V. (2025). Leveraging artificial intelligence and machine learning in sport sciences: A systematic literature review of applications, outcomes, and future directions. Sport Sciences for Health, 21(4), 2429–2443. https://doi.org/10.1007/s11332-025-01482-y
21. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
22. Malatyalı, M. A. (2021). A longitudinal investigation of teachers’ self-efficacy and students’ skills in teaching computational thinking [Unpublished Master’s thesis]. Van Yüzüncü Yıl University.
23. Mart, M., & Kaya, G. (2024). The relationship between preschool teacher candidates’ attitudes toward artificial intelligence and their AI literacy. Edutech Research, 2(1), 91–109.
24. McArdle, W. D., Katch, F. I., & Katch, V. L. (2015). Exercise physiology: Nutrition, energy, and human performance (8th ed.). Pearson.
25. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
26. Olpak, T., & Karamete, A. (2024). The Relationship Between Gifted Students' Self-Efficacy Perceptions of Computational Thinking Skills and Information Technologies Self-Efficacy Perceptions. Necatibey Faculty of Education Electronic Journal of Science and Mathematics Education, 18(1), 148-174. https://doi.org/10.17522/balikesirnef.1378877
27. Paf, M. (2019). The relationship between middle school students’ computational thinking skills and creative problem-solving skills [Unpublished Master’s thesis]. Adnan Menderes University.
28. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
29. Sanchez, J. (2011). Experiencing the creative problem-solving thinking skills model in virtual worlds [Unpubhished Master’s project]. Buffalo State College.
30. Sert Orhan, M. (2023). Preservice teachers’ lifelong learning tendencies [Unpublished Master’s thesis]. Aydın Adnan Menderes University.
31. Soylu, E. (2023). The effect of artificial intelligence educational content [Unpublished Master’s thesis]. Sakarya University.
32. Subaşı, E., & Akhan, N. E. (2024). Computational thinking among social studies preservice teachers. Amasya University Journal of the Faculty of Education, 13(1), 41–71. https://doi.org/10.17539/amauefd.1437344
33. Wang, B., Rau, P.-L. P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: Validity and reliability of the artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
34. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
35. Yıldız, A. G. M., Çiftçi, E., & Karal, H. (2017). Computational thinking and programming. In H. F. Odabaşı, B. Akkoyunlu, & A. İşman (Eds.), Eğitim teknolojileri okumaları 2017 (pp. 75–86). TOJET.
36. Zhou, D., Keogh, J. W. L., Ma, Y., Tong, R. K. Y., Khan, A. R., & Jennings, N. R. (2025). Artificial intelligence in sport: A narrative review of applications, challenges and future trends. Journal of Sports Sciences, 1–16. https://doi.org/10.1080/02640414.2025.2518694