休闲运动员对人工智能生成的运动计划的接受和信任以及经验丰富的教练的质量评估:一项试点研究。

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES
Felix Wachholz, Stefano Manno, Daniel Schlachter, Nicole Gamper, Martin Schnitzer
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引用次数: 0

摘要

目标:大型语言模型正在成为日常生活中越来越重要的工具,包括训练和运动的背景。然而,休闲运动员在多大程度上真正依赖于人工智能生成的训练计划,以及用户和非用户之间对这些技术的信任差异,尚未得到调查。此外,缺乏关于这种人工智能生成的培训计划当前质量的信息。该项目的目的是研究用户和非用户对这些技术的信任程度如何不同,并评估人工智能生成的培训计划的质量。结果:在我们的样本中,54%的参与者使用结构化培训计划进行培训,其中25%的参与者使用人工智能生成的培训计划。与非用户相比,这些基于人工智能的工具的用户对这些技术的信任程度显著(p = 0.030)更高。大型语言模型的输出质量现在已经达到了一个水平,即使是专业教练也常常无法区分训练计划是由人工智能生成的还是由人类专家创建的。这表明,人工智能生成的训练计划可能会达到经验丰富的教练制定的标准,使其成为运动员在训练中寻求指导的可行选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acceptance and trust in AI-generated exercise plans among recreational athletes and quality evaluation by experienced coaches: a pilot study.

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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来源期刊
BMC Research Notes
BMC Research Notes Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
3.60
自引率
0.00%
发文量
363
审稿时长
15 weeks
期刊介绍: BMC Research Notes publishes scientifically valid research outputs that cannot be considered as full research or methodology articles. We support the research community across all scientific and clinical disciplines by providing an open access forum for sharing data and useful information; this includes, but is not limited to, updates to previous work, additions to established methods, short publications, null results, research proposals and data management plans.
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