人工智能驱动的体育教练员的特征和感知适宜性:心理和感知因素的初步研究。

IF 2.3 Q2 SPORT SCIENCES
Frontiers in Sports and Active Living Pub Date : 2025-05-12 eCollection Date: 2025-01-01 DOI:10.3389/fspor.2025.1548980
Carlo Dindorf, Jonas Dully, Eva Bartaguiz, Tessa Menges, Claudia Reidick, Johann-Nikolaus Seibert, Michael Fröhlich
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引用次数: 0

摘要

引言:获得人类体育教练往往受到财政和后勤障碍的限制,导致高质量教练的可用性存在差异。由大型语言模型(llm)驱动的人工智能(AI)教练可能提供有前途的方法,通过支持或自主执行目标领域内的特定教练任务来增强人类教练。本研究通过解决三个主要问题,调查了人工智能教练的相关属性和训练背景下的感知适应性:(A)语义差异量表上的哪些属性有效地描述了训练支持背景下人工智能教练的维度?(B)通过语义差异量表测量,对人工智能适合其训练实践的不同看法的参与者在与人工智能教练相关的属性上是否存在差异?(C)不同的个人成就动机(AMS)-运动是否影响对人工智能教练适用性的感知?方法:本研究分为两部分。第一项研究涉及开发语义差异量表,以量化人工智能教练的感知,并分析使用法学硕士设计的不同人工智能教练个性如何感知其训练适用性,以及成就动机如何影响这些感知。为了反映不同的教练风格,我们创造了六种不同的AI教练个性。结果:因子分析揭示了人工智能教练属性的四个关键维度:知识转移、目标导向持久性、欣赏和认可、动机支持。结果表明,与那些被评为不太合适的教练相比,被评为更合适的教练表现出了支持性的特征,如动机和目标导向。害怕失败(FoF)程度较低的参与者也倾向于认为人工智能教练更合适。结论:这些发现强调了将人工智能教练的特征与其动机档案相结合以提高用户参与度的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characteristics and perceived suitability of artificial intelligence-driven sports coaches: a pilot study on psychological and perceptual factors.

Introduction: Access 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?

Methods: The 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.

Results: Factor 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.

Conclusion: These findings underscore the importance of aligning AI coaches' characteristics with their motivational profiles to improve user engagement.

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来源期刊
CiteScore
2.60
自引率
7.40%
发文量
459
审稿时长
15 weeks
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