ArticleBiology of sport2026
The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.
Article in Biology of sport, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- The AI recommendation paradox: a systematic review evaluating the promise, peril, and path forward for large language models in exercise recommendation.Biology of sport · 2026Review
- Physiological relevance and generation-level stability of artificial intelligence-generated race-day warm-up and cool-down plans for sprint swimming: an expert-rated comparison of four large language models.Frontiers in physiology · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AI-based chatbots are increasingly used to design training programs, but their effectiveness for elite athletes is unclear. This study assessed ChatGPT-4's ability to generate weekly training plans for elite swimmers and sprinters. Twenty-three coaches and thirty-six athletes rated the AI-generated plans using a 5-point Likert scale in three areas: weekly frequency, intensity adjustments, and training structure. Seven intensity zones were analyzed: A1 (endurance/recovery), A2 (extensive aerobic), B1 (intensive aerobic), B2 (aerobic-anaerobic transition), C1 (anaerobic threshold), C2 (anaerobic-lactate), and C3 (maximal sprint intensity). Coaches gave neutral-to-positive ratings (3.6 for distance swimmers, 3.7 for sprinters), while athletes were more critical (2.8 and 3.1, respectively). AI-generated plans performed well in low-intensity zones (A1) but had shortcomings in moderate-intensity (A2, B1-B2: long repetitions, excessive sets, insufficient recovery) and anaerobic zones (C1: excessive frequency for swimmers; C2-C3: insufficient frequency for sprinters). No significant differences emerged between plans for swimmers and sprinters (p=0.596), but A2, B1, and B2 showed greater discrepancies (p < 0.001). Rating reliability was moderate for coaches (ICC=0.609) and low for athletes (ICC=0.369). Older coaches and male athletes rated the plans lower, while those with national-level experience were more favorable. While 65% of coaches found the plans usable with minor modifications, only 27.8% of athletes agreed, 47.2% requested major changes, and 25% rejected them. ChatGPT-4 is useful for simple training plans but requires human supervision for complex periodization, particularly in high-intensity zones.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.