CORRECTION article
Front. Artif. Intell.
Sec. AI for Human Learning and Behavior Change
Volume 8 - 2025 | doi: 10.3389/frai.2025.1710897
This article is part of the Research TopicAI Innovations in Education: Adaptive Learning and BeyondView all 23 articles
Correction: Artificial intelligence-enhanced assessment of fundamental motor skills: validity and reliability of the FUS test for jumping rope performance
Provisionally accepted
Hubert Makaruk1*
Porter J. M.2
E. Kipling Webster2B Makaruk1P Tomaszewski1
Marta Nogal1
Daniel Gawlowski3
Łukasz Sobański4
Bartosz Molik1J Sadowski1- 1Józef Piłsudski University of Physical Education in Warsaw, Warsaw, Poland
- 2University of Tennessee, Knoxville, TN, United States, Knoxville, United States
- 3Artificial Intelligence Department, DG Consulting, Wrocław, Poland, Wrocław, Poland
- 4Department of Research in Artificial Intelligence, Instat sp. z o.o., Wrocław, Poland, Wrocław, Poland
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In the published article, there was a mistake in the Abstract. The wrong version of the Abstract was inserted by the author.The corrected Abstract should read:Introduction: Widespread concerns about children's low fundamental motor skill (FMS) proficiency highlight the need for accurate assessment tools to support structured instruction. This study examined the validity and reliability of an AIenhanced methodology for assessing jumping rope performance within the Fundamental Motor Skills in Sport (FUS) test. Methods: A total of 236 participants (126 primary school students aged 7-14; 110 university sports students aged 20-21) completed jumping rope tasks recorded via the FUS mobile app integrated with an AI model evaluating five process-oriented performance criteria. Concurrent validity and inter-rater reliability were examined by comparing AI-generated assessments with scores from two expert evaluators. Intra-rater reliability was also assessed through reassessment of video trials after a three-week interval. Results: Results revealed excellent concurrent validity and inter-rater reliability for the AI model compared with expert ratings (ICC = 0.96; weighted kappa = 0.87). Agreement on individual criteria was similarly high (Cohen's kappa = 0.83-0.87). Expert-adjusted AI scores further improved reliability (ICC = 0.98). Intrarater reliability was also excellent, with perfect agreement for AI-generated scores (ICC = 1.00; kappa = 1.00). Conclusions: These findings demonstrate that AI-based assessment offers objective, reliable, and scalable evaluation, enhancing accuracy and efficiency of FMS assessment in education and research.The original version of this article has been updated.
Keywords: motor competence, Fundamental movement skills, machine learning, Mobile application, Physical Education
Received: 22 Sep 2025; Accepted: 08 Oct 2025.
Copyright: © 2025 Makaruk, M., Kipling Webster, Makaruk, Tomaszewski, Nogal, Gawlowski, Sobański, Molik and Sadowski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hubert Makaruk, hubert.makaruk@awf.edu.pl
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