THE USE OF AI DETECTORS IN PEDAGOGICAL PRACTICE WITHIN PHYSICAL EDUCATION AND SPORTS
DOI:
https://doi.org/10.62405/osiconf.2026.06Keywords:
artificial intelligence detectors; generative artificial intelligence; academic integrity; physical education and sports.Abstract
Purpose. To empirically assess the reliability and consistency of modern artificial intelligence detectors when identifying the origin of completed specific independent tasks in the field of physical education and sports. Methods. The case study method was applied. Three versions of the training complex were developed – the original one, generated by GPTchat, and humanized using Grammarly tools, which were tested by 19 AI detectors. Results. Extremely low consistency of indicators was established (variation from 0% to 96%), the absence of an unambiguous humanization effect and the risk of false positive detection of human text. Conclusions. The feasibility of assessing practice-oriented tasks instead of relying on a separate or several automated detectors was substantiated. A promising direction for further development of this topic is the formation of an approach to the assessment of practice-oriented tasks, combining several independent detectors and oral defense of the justification of the educational product.
References
Chaka, C. (2024). Reviewing the performance of AI detection tools in differentiating between AI-generated and human-written texts. Journal of Applied Learning & Teaching, 7(1), 115–126. https://doi.org/10.37074/jalt.2024.7.1.25
Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19(1), Article 17. https://doi.org/10.1007/s40979-023-00140-5
Erol, G., Ergen, A., Gülşen Erol, B., Kaya Ergen, Ş., Bora, T. S., Çölgeçen, A. D., & Güngör, A. (2025). Can we trust academic AI detective? Accuracy and limitations of AI-output detectors. Acta Neurochirurgica, 167(1), Article 214. https://doi.org/10.1007/s00701-025-06622-4
Hadra, M., Cambridge, K., & Mesbah, M. (2026). Evaluating the accuracy and reliability of AI content detectors in academic contexts. International Journal for Educational Integrity, 22(4), 1–18. https://doi.org/10.1007/s40979-026-00213-1
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), Article 100779. https://doi.org/10.1016/j.patter.2023.100779
Paullet, K., Pinchot, J., Kinney, E., & Stewart, T. (2025). Precision check: A critical look at the reliability of AI detection tools. Issues in Information Systems, 26(3), 400–412. https://doi.org/10.48009/3_iis_2025_2025_132
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 1–24. https://doi.org/10.53761/1.20.02.07
Perkins, M., Furze, L., & Roe, J. (2024). Simple techniques to bypass GenAI text detectors: Implications for inclusive education. International Journal of Educational Technology in Higher Education, 21(1), Article 53. https://doi.org/10.1186/s41239-024-00487-w
Shekhar, A. R., Agarwal, S., Bordoloi, P., Shah, Y., Anvekar, T., & Gupta, V. (2026). DoPE: Decoy Oriented Perturbation Encapsulation Human-Readable, AI-Hostile Documents for Academic Integrity (arXiv:2601.12505). arXiv. https://doi.org/10.48550/arXiv.2601.12505
Walters, W. H. (2023). The effectiveness of software designed to detect AI-generated writing: A comparison of 16 AI text detectors. Open Information Science, 7(1), Article 20220158. https://doi.org/10.1515/opis-2022-0158
Weber-Wulff, D., Anohina-Naumeca, A., & Bjelobaba, S. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 1–39. https://doi.org/10.1007/s40979-023-00146-z