Enhancing action recognition in educational settings using AI-driven information systems for public health monitoring

The integration of Artificial Intelligence (AI) into educational environments is revolutionizing action recognition, offering a transformative opportunity to enhance public health monitoring. Traditional methods, which primarily rely on rule-based algorithms or handcrafted feature extraction, face significant challenges in adaptability, scalability, and real-time processing. These limitations hinder their effectiveness, particularly in detecting health-related behaviors such as sedentary patterns, social interactions, and hygiene compliance.

To overcome these shortcomings, this research introduces an AI-driven information system that leverages advanced deep learning models and an Adaptive Knowledge Embedding Network (AKEN) to improve action recognition accuracy. The approach integrates AKEN with a Dynamic Personalized Learning Strategy (DPLS) to model student behaviors, predict future actions, and optimize intervention strategies by incorporating factors such as engagement levels, learning progress, and environmental conditions.

By utilizing reinforcement learning and explainable AI techniques, the system not only refines recognition accuracy but also ensures transparency in decision-making. Real-time engagement monitoring enhances adaptability, allowing educators and policymakers to make informed interventions.

Experimental results validate the system's superior performance over conventional approaches, demonstrating its ability to recognize complex behavioral patterns in educational settings. This innovation represents a significant step forward in AI-driven public health monitoring, fostering a safer and more responsive learning environment.