Artificial Intelligence in Physical Education: A New Hybrid Decision-Making Model

Strategic Insights -

A recent study from Shandong University has introduced a novel hybrid decision-making model that incorporates the weighted aggregated sum product assessment (WASPAS) technique into the q-rung linear Diophantine fuzzy set (q-RLDFS) framework to select the most suitable artificial intelligence (AI) algorithms in uncertain and vague environments. 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 physical education. The proposed model presents a two-layer framework, enabling a flexible scoring system and high-order fuzzy modelling, making the decision results more interpretable and robust.

Key Takeaways:

  • The study 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 to select the most suitable AI algorithms in uncertain and vague environments.
  • The model presents a two-layer framework, enabling a flexible scoring system and high-order fuzzy modelling, making the decision results more interpretable and robust.
  • The study evaluates 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) against eight key criteria relevant to physical education.
  • 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 physical education domain.

STATISTICS:

  • The study evaluates five AI algorithms against eight key criteria relevant to physical education.
  • Results revealed that CNN-MA is the most effective solution to implement, followed by RL-TO.
  • The model presents a two-layer framework, enabling a flexible scoring system and high-order fuzzy modelling.
  • The study addresses 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 physical education.

Sources:

  • A hybrid q-rung linear diophantine fuzzy WASPAS approach for artificial intelligence algorithm selection in physical education. Scientific Reports, 2025;15(1):32200.
  • NewsRx. Study Findings from Shandong University Provide New Insights into Artificial Intelligence (A hybrid q-rung linear diophantine fuzzy WASPAS approach for artificial intelligence algorithm selection in physical education). Education Letter. September 17, 2025; p 887.