The Investigation of Artificial Intelligence Readiness Levels of Physical Education and Sport Sciences Teacher Candidates
DOI:
https://doi.org/10.5281/zenodo.14568852Keywords:
Artificial Intelligence , Physical Education , Teacher CandidatesAbstract
The aim of this study was to examine the opinions of sport sciences faculty students on the use of wearable technological products. The aim of this study is to examine the opinions of sport sciences faculty students on the use of wearable technological products. In this direction, a total of 100 students studying at the faculty of sport sciences voluntarily participated in the study. In addition to the personal information form created by the researchers, the attitude scale towards wearable technological sports products (ASTWTSP) was used as a data collection tool. Kruskal Wallis H and Mann Whitney U tests were used for nonparametric data. In the statistical analysis of the data obtained after the study and in determining the differences between the groups, p<0.05 value will be considered significant. According to the findings; the total scores of the attitude scale towards wearable technological sports products of the participants did not show a significant difference according to their gender (U=1192.00; p=0.781; p>0.05), did not show a significant difference according to age variable (X2=0,237; p=0,888; p>0.05) and did not show a statistically significant difference according to income variable (X2=4,516; p=0,105; p>0.05). As a result, based on the results obtained from our study and the literature, and considering the various advantages of wearable technological products, it can be stated that wearable technologies are a complement to the complex structure of exercise or sports on individuals.
References
Lee, H.S., & Lee, J. (2021). Applying Artificial Intelligence in Physical Education and Future Perspectives. Sustainability, 13, 351. [Crossref]
McArthur, D., Lewis, M., & Bishary, M. (2005). The Roles of Artificial Intelligence In Education: Current Progress And Future Prospects.I-Manag. J. Educ. Technol, 1, 42–80. [Crossref]
Emmert-Streib, F., Yli-Harja, O. & Dehmer, M. (2020). Artificial intelligence: a clarification of misconceptions, myths and desired status. Front Artif Intell.; 3:524339. [PubMed]
Zhang, Y., Duan, W., Villanueva, L. E. & Chen, S. (2023). Transforming sports training through the integration of internet technology and artificial intelligence. Soft Comp.; 27:15409–23. [Crossref]
Wang, Y. & Wang, X. (2024). Artificial intelligence in physical education: comprehensive review and future teacher training strategies. Front. Public Health 12:1484848. [PubMed]
Lee, H. S. & Lee, J. (2021). Applying artificial intelligence in physical education and future perspectives. Sustainability, 13:351. [Crossref]
Deng, C., Feng, L. & Ye, Q. (2023). Smart physical education: governance of school physical education in the era of new generation of information technology and knowledge. J KnowlEcon, 8:1–33. [Crossref]
Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 42(9), 1324-1337. [Crossref]
Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC medical education, 21, 1-9. [Crossref]
Ozudoğru, G. & Yildiz Durak, H. (2024). Turkish Adaptation of the AI Readiness Scale for Preservice Teachers. 10th New York International Congress on Academic Studies in Social, Human, Administrative and Educational Sciences, 1-9.
Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education. International Journal of Artificial Intelligence in Education, 32(3), 725- 755.
Yildiz Durak, H., & Onan, A. (2024). Predicting the use of chatbot systems in education: a comparative approach using PLS-SEM and machine learning algorithms. Current Psychology, 1-19.
Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS)–development, validity and reliability study. BMC medical education, 21, 1-9. [Crossref]
Xian, L. Artificial intelligence and modern sports education technology. In Proceedings of the 2010 International Conference on Artificial Intelligence and Education (ICAIE), Hangzhou, China, 29–30 October 2010; pp. 772–776. [Crossref]
Kim, Y. S. (2017). Daegu National University of Education. Elementary School Teachers’ and Teacher Educators’ Ideas of English Education in the 4th Industrial Society. Inst. Educ. Res. Gyeongin Natl. Univ. Educ.; 37, 123–150.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Mert DOĞAN, Erick BURHAEIN, Ali Md NADZALAN, Diajeng Tyas Pinru PHYTANZA
This work is licensed under a Creative Commons Attribution 4.0 International License.