Abstract
Objectives: Large language models are becoming increasingly significant tools in everyday life, including the context of training and sports. However, the extent to which recreational athletes actually rely on AI-generated training plans and the differences in trust towards these technologies between users and non-users have not yet been investigated. Furthermore, there is a lack of information regarding the current quality of such AI-generated training plans. The aim of this project was to examine how users and non-users differ in their trust towards these technologies and to assess the quality of AI-generated training plans. Results: In our sample, 54% of the participants trained using a structured training plan, with 25% of those utilizing AI-generated training plans. Users of these AI-based tools exhibited significantly (p = 0.030) higher levels of trust in these technologies compared to non-users. The quality of the output from large language models has now reached a level where even professional coaches are often unable to distinguish whether a training plan was AI-generated or created by a human expert. This suggests that AI-generated training plans could potentially match the standards of those developed by experienced coaches, making them a viable option for athletes seeking guidance in their training.
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Wachholz, F., Manno, S., Schlachter, D., Gamper, N., & Schnitzer, M. (2025). Acceptance and trust in AI-generated exercise plans among recreational athletes and quality evaluation by experienced coaches: a pilot study. BMC Research Notes, 18(1). https://doi.org/10.1186/s13104-025-07172-9
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