Assessment of Artificial Intelligence-Driven Fitness and Health Management Programs for Adolescents Using the SuperHyperSoft Set Framework

The rising use of artificial intelligence in adolescent fitness and health applications has created a need for more sophisticated evaluation frameworks. These platforms often operate in complex, dynamic environments where outcomes depend on behavioral, emotional, and contextual factors. Traditional evaluation models fail to fully capture this complexity. In this study, we apply the SuperHyperSoft Set (SHSS) framework to assess five AI-based health platforms targeted at adolescents. SHSS provides a multi-layered structure for organizing and analyzing evaluation criteria, while also allowing experts to express uncertainty and disagreement in a mathematically consistent way. Through a real-world case study, we demonstrate how the model supports a more nuanced and interpretable evaluation. The results show a high alignment between the model’s rankings and expert judgments, validating its effectiveness. The study also includes sensitivity analysis to confirm the robustness of the approach. The findings offer valuable guidance for developers, public health managers, and educators working at the intersection of AI and adolescent wellness.