Ni Yuanzhen, Wang Fei, Zhang Hongzhen, Kim Sung-Min
Sports and Artificial Intelligence, School of Physical Education, Shandong University, Jinan, 250061, China.
Sports Psychology, School of Arts and Sports, Hanyang University, Seoul, 04763, South Korea.
Sci Rep. 2025 Sep 1;15(1):32200. doi: 10.1038/s41598-025-17833-1.
The growing use of artificial intelligence (AI) in physical education (PE) has led to an urgent need to develop robust methodologies that can be used to choose the most suitable algorithms in uncertain and vague environments. This paper introduces a new hybrid decision-making (DM) model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework. The primary objective is to address the gap in the lack of structured and uncertainty-resistant methods for assessing AI models based on multiple, frequently conflicting criteria in the domain of PE. The proposed model presents a two-layer framework, where the WASPAS method enables a flexible scoring system by combining both the weighted sum model (WSM) and the weighted product model (WPM). On the other hand, the q-RLDFS framework facilitates high-order fuzzy modelling and accounts for hesitation in expert ratings, thereby making the decision results more interpretable and robust. To determine the effectiveness of the model, five AI algorithms named convolutional neural network-based motion analysis (CNN-MA), reinforcement learning based training optimizer (RL-TO), expert system for exercise prescription (ES-EP), hybrid AI tutor with natural language processing (HAI-NLP), and wearable sensor data mining algorithm (WSDMA) are evaluated against eight key criteria relevant to PE. Results revealed that CNN-MA is the most effective solution to implement, followed by RL-TO. The sensitivity and comparative analysis are thoroughly conducted to determine the validity of the model in terms of its robustness and reliability. The research offers distinct practical implications and actionable recommendations to educators, administrators, and policymakers, informing the strategic implementation of AI technologies within the PE domain. In general, the research makes a significant contribution to the adoption of AI in PE, as it provides a scalable, transparent, and practically applicable decision-support model.
人工智能（AI）在体育教育（PE）中的应用日益广泛，这使得迫切需要开发强大的方法，以便在不确定和模糊的环境中选择最合适的算法。本文介绍了一种新的混合决策（DM）模型，该模型将加权聚合和积评估（WASPAS）技术纳入q阶线性丢番图模糊集（q-RLDFS）框架。主要目标是解决体育教育领域中基于多个经常相互冲突的标准评估人工智能模型时，缺乏结构化和抗不确定性方法的问题。所提出的模型呈现出一个两层框架，其中WASPAS方法通过结合加权和模型（WSM）和加权积模型（WPM）实现了灵活的评分系统。另一方面，q-RLDFS框架促进了高阶模糊建模，并考虑了专家评级中的犹豫因素，从而使决策结果更具可解释性和稳健性。为了确定该模型的有效性，针对与体育教育相关的八个关键标准，对基于卷积神经网络的运动分析（CNN-MA）、基于强化学习的训练优化器（RL-TO）、运动处方专家系统（ES-EP）、具有自然语言处理功能的混合人工智能导师（HAI-NLP）和可穿戴传感器数据挖掘算法（WSDMA）这五种人工智能算法进行了评估。结果表明，CNN-MA是最有效的实施解决方案，其次是RL-TO。进行了敏感性和比较分析，以确定该模型在稳健性和可靠性方面的有效性。该研究为教育工作者﹑管理人员和政策制定者提供了独特的实际意义和可操作的建议，为体育教育领域人工智能技术的战略实施提供了参考。总体而言，该研究为体育教育中人工智能的应用做出了重大贡献，因为它提供了一个可扩展、透明且实际适用的决策支持模型。
