Stress Detection and Management for Students Using AI and IoT Sensors

Student stress is a growing concern in educational environments, affecting academic performance, mental health, and overall well-being. Traditional methods of stress detection, such as self-report surveys, are limited in their ability to provide real-time, accurate assessments. The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has emerged as a promising solution to address these limitations. AI algorithms, in conjunction with IoT sensors, can continuously monitor physiological markers, such as heart rate, skin conductance, and sleep patterns, to detect stress in real-time. This chapter explores the applications of AI and IoT in stress detection and management within educational settings, highlighting the potential of these technologies to provide personalized, data-driven interventions. Through case studies of successful implementations, this chapter demonstrates how AI and IoT systems can enhance student well-being, improve mental health outcomes, and optimize academic performance. The challenges of sensor calibration, data accuracy, and user adoption are discussed, along with solutions to overcome these barriers. By examining the role of cloud and edge computing in stress data management, this chapter offers a comprehensive understanding of how AI and IoT can revolutionize student support systems. The integration of these technologies presents an opportunity to proactively manage student stress, ensuring a healthier and more supportive educational environment.