Correction: Artificial intelligence-enhanced assessment of fundamental motor skills: validity and reliability of the FUS test for jumping rope performance
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".