The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming

Luca Puce; Piotr Żmijewski; Filippo Cotellessa; Cristina Schenone; Halil I. Ceylan; Nicola L. Bragazzi; Carlo Trompetto

doi:10.5114/biolsport.2026.152352

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

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abstract:

Original paper

The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming

Luca Puce

^{1, 2}

,

Piotr Żmijewski

³

,

Filippo Cotellessa

^{1, 2}

,

Cristina Schenone

^{1, 2}

,

Halil I. Ceylan

⁴

,

Nicola L. Bragazzi

^{1, 2}

,

Carlo Trompetto

^{1, 2}

Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
IRCCS Ospedale Policlinico San Martino, Genoa, Italy
Jozef Pilsudski University of Physical Education in Warsaw, Poland
Physical Education and Sports Teaching Department, Faculty of Sports Sciences, Ataturk University, Erzurum, Turkey

Biol Sport. 2026;43:355–367

DOI: https://doi.org/10.5114/biolsport.2026.152352

Online publish date: 2025/09/16

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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.

keywords:

The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming

Luca Puce 1, 2 , Piotr Żmijewski 3 , Filippo Cotellessa 1, 2 , Cristina Schenone 1, 2 , Halil I. Ceylan 4 , Nicola L. Bragazzi 1, 2 , Carlo Trompetto 1, 2

Natural language processing, AI-driven chatbots, Periodisation phase, Sprinters, Distance swimmers

Luca Puce

^{1, 2}

,

Piotr Żmijewski

³

,

Filippo Cotellessa

^{1, 2}

,

Cristina Schenone

^{1, 2}

,

Halil I. Ceylan

⁴

,

Nicola L. Bragazzi

^{1, 2}

,

Carlo Trompetto

^{1, 2}