Evaluating the Impact of IoT and AI-Based Health Education on Students' Knowledge and Behavior

The knowledge and good behaviour that can be instilled in students are important for their physical, mental, emotional, and social health in the face of emerging infectious diseases. Traditional health education methods fall short of the levels of engagement and personalization needed for the increasingly dispersed populations in modern times. In this respect, IoT and AI present exciting new opportunities for meaningful health education tailored to the needs of the students. The research will bridge the gap by examining how IoT- and AI-driven health education can improve students' infectious disease prevention awareness and behaviour. In this research, an IoT- and AI-enabled health education framework has been developed that uses health education-based multiple linear regression (HE-MLR) analysis to determine the impact factor on students' knowledge and behaviour regarding infectious diseases. IoT-enabled health surveillance systems provide feedback, interactive content, and guidance on best practices in preventive health to students in real-time. In this regard, the proposed HE-MLR technique evaluates the effectiveness of IoT and AI-based health education in bringing behavioural change related to respiratory infectious diseases among targeted students. To that end, one more DID analysis is used to estimate the overall impact of the proposed interventions on students' health behaviours. These findings support that IoT- and AI-enhanced health education significantly improves students' knowledge, attitudes, and actions toward healthier practices. Other factors such as seminars, visual aids, and interactive media enhance the health messages. Thus, HE-MLR serves as a viable model for creating better health outcomes for students.