O'Brien Katherine A, Prentice Sarah
School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
Front Sports Act Living. 2025 Jul 29;7:1627685. doi: 10.3389/fspor.2025.1627685. eCollection 2025.
This study represents one of the initial efforts to analyse a coach-athlete conversational dataset using freely available GPT tools and a pre-determined, context-specific, prompt-based analyses framework (i.e., R-PIASS). One dialogue dataset was selected by means of two different freely available AI-based GPT tools: ChatGPT v4 and DeepSeek v3. The results illustrated that both ChatGPT v4 and DeepSeek v3 models could extract quantitative and qualitative conversational information from the source material using simple R-PIASS prompt specifiers. Implications for how coaches can use this technology to support their own learning, practice designs, and performance analyses were the efficiencies both platforms provided in relation to cost, usability, accessibility and convenience. Despite the strengths, there were also associated risks and pitfalls when using this process such as the strength and robustness of the applicable statistical outcomes and tensions between keeping the input data within the context and ensuring that the context did not breach privacy issues. Further investigations that engage GPT platforms for coach-athlete dialogue analysis are therefore required to ascertain the true relevance and potential of using this type of technology to enhance coach learning and athlete development.
本研究是利用免费的GPT工具和一个预先确定的、针对特定情境的、基于提示的分析框架(即R-PIASS)来分析教练-运动员对话数据集的初步尝试之一。通过两种不同的免费基于人工智能的GPT工具:ChatGPT v4和DeepSeek v3,选择了一个对话数据集。结果表明,ChatGPT v4和DeepSeek v3模型都可以使用简单的R-PIASS提示说明符从源材料中提取定量和定性的对话信息。两个平台在成本、可用性、可及性和便利性方面所提供的效率,说明了教练如何利用这项技术来支持他们自己的学习、实践设计和表现分析。尽管有这些优势,但使用这个过程也存在相关的风险和陷阱,比如适用统计结果的强度和稳健性,以及在保持输入数据在情境范围内与确保情境不违反隐私问题之间的矛盾。因此,需要进一步利用GPT平台进行教练-运动员对话分析的研究,以确定使用这类技术来促进教练学习和运动员发展的真正相关性和潜力。