Commercializing Deep Learning and Biotechnological Approaches in Physical Education Management: Analytical Strategies and Case Studies

Abstract

Objective: Physical education (PE) is crucial in human development. This study aims to assess the efficacy of a PE program that integrates biotechnology and deep learning, examining its impact on student fitness outcomes and engagement compared to a traditional PE program. Methods: A quasi-experimental study was conducted at a high school in Shanghai involving 350 students. Participants were divided into an intervention group, which received a PE program enhanced with biotechnological tools and deep learning algorithms, and a control group which followed a standard PE curriculum. The program lasted for 12 weeks. Results: The intervention group showed a statistically significant improvement in cardiorespiratory fitness, with VO2 max increasing from 40.2 ± 5.1 mL/kg/min to 44.8 ± 4.3 mL/kg/min (t = 2.8, p = 0.003). Additionally, there was a notable increase in strength, as measured by the 1RM squat test, with the intervention group improving by 8.3 compared to 5.1 in the control group (t = 3.2, p = 0.002). Conclusion: The integration of biotechnology and deep learning into PE programs can significantly enhance student fitness, reduce injury risk, and boost engagement and motivation. This approach not only demonstrates improved educational outcomes but also holds substantial commercial potential. The application of these technologies in PE programs could lead to the development of new educational products and services, presenting lucrative opportunities for businesses in the educational technology sector. Future research should explore the scalability of these interventions and their applicability in different educational settings, underscoring their potential to redefine physical education through technological innovation.