Characteristics and perceived suitability of artificial intelligence-driven sports coaches

A Pilot Study on Psychological and Perceptual Factors

Bibliographic Data

ID5284978
AuthorsCarlo Dindorf (0000-0003-0378-8481, University of Kaiserslautern), Jonas Dully (0000-0002-6822-0697, University of Kaiserslautern), Eva Bartaguiz (0000-0001-7263-668X, University of Kaiserslautern), Tessa Menges, Claudia Reidick (University of Kaiserslautern), Johann-Nikolaus Seibert (University of Kaiserslautern), Michael Fröhlich (0000-0001-8439-8746, University of Kaiserslautern)
Year2025
Volume7
Pages1548980-1548980
Publication date2025-05-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Sports and Active Living (JOURNAL)
Journal identifiersISSN: 2624-9367 • E-ISSN: 2624-9367
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fspor.2025.1548980
PMID40421103
OpenAlexW4410281895
LanguageEN
Citations received1
References cited59

IntroductionAccess to human sports coaches is often limited by financial and logistical barriers, leading to disparities in the availability of high-quality coaches. Artificial intelligence (AI) coaches powered by Large Language Models (LLMs) might offer promising means to augment human coaches by supporting or autonomously performing specific coaching tasks within targeted domains. This study investigated AI coaches' associated attributes and perceived suitability in training contexts by addressing three primary questions: (A) Which attributes on a semantic differential scale effectively describe the dimensions of AI coaches in the context of training support? (B) Do participants with varying perceptions of AI suitability for their training practices differ in the attributes they associate with AI coaches, as measured by a semantic differential scale? (C) Do different individual achievement motives (AMS)-Sport influence the perception of AI coaches' suitability?MethodsThe study comprised two parts. The first involves the development of a semantic differential scale to quantify the perceptions of AI coaches and an analysis of how different AI coach personalities, designed using an LLM, are perceived concerning their training suitability and how achievement motives influence these perceptions. Six distinct AI coach personalities were created to reflect the diverse coaching styles.ResultsFactor analysis revealed four key dimensions of AI coach attributes: knowledge transfer, goal-oriented persistence, appreciation and recognition, and motivational support. The results indicated that coaches rated as more suitable exhibited supportive traits, such as motivation and goal orientation, compared to those rated less suitable. Participants with a lower Fear of Failure (FoF) also tended to rate AI coaches as more appropriate.ConclusionThese findings underscore the importance of aligning AI coaches' characteristics with their motivational profiles to improve user engagement

Athletes · Coaching · Cognitive psychology · Perception · Physical therapy · Psychotherapist · Applied Psychology · Behavioral Health and Interventions · Medicine · Motivation and Self-Concept in Sports · Psychology · Sport Psychology and Performance

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