ChatGPT generated training plans for runners are not rated optimal by coaching experts, but increase in quality with additional input information

ChatGPT may be used by runners to generate training plans to enhance performance or health aspects. However, the quality of ChatGPT generated training plans based on different input information is unknown. The objective of the study was to evaluate ChatGPT-generated six-week training plans for runners based on different input information granularity. Three training plans were generated by ChatGPT using different input information granularity. 22 quality criteria for training plans were drawn from the literature and used to evaluate training plans by coaching experts on a 1-5 Likert Scale. A Friedmann test assessed significant differences in quality between training plans. For training plans 1, 2 and 3, a median rating of <3 was given 19, 11, and 1 times, a median rating of 3 was given 3, 5, and 8 times and a median rating of >3 was given 0, 6, 13 times, respectively. Training plan 1 received significantly lower ratings compared to training plan 2 for 3 criteria, and 15 times significantly lower ratings compared to training plan 3 (p < 0.05). Training plan 2 received significantly lower ratings (p < 0.05) compared to plan 3 for 9 criteria. ChatGPT generated plans are ranked sub-optimally by coaching experts, although the quality increases when more input information are provided. An understanding of aspects relevant to programming distance running training is important, and we advise avoiding the use of ChatGPT generated training plans without an expert coach`s feedback.
© Copyright 2024 Journal of Sports Science & Medicine. Department of Sports Medicine - Medical Faculty of Uludag University. All rights reserved.

Bibliographic Details
Subjects:
Notations:biological and medical sciences technical and natural sciences
Tagging:künstliche Intelligenz
Published in:Journal of Sports Science & Medicine
Language:English
Published: 2024
Online Access:https://doi.org/10.52082/jssm.2024.56
Volume:23
Issue:1
Pages:56-72
Document types:article
Level:advanced