Stress has emerged as a critical factor influencing student academic performance, classroom participation, and overall emotional well-being. Early identification of stress is essential for implementing adaptive learning strategies and ensuring timely intervention to improve both mental health and educational outcomes. Conventional assessment approaches, such as self-report questionnaires, observational analysis, and psychological surveys, often lack scalability, objectivity, and the ability to capture real-time variations in student behaviour. Recent advances in artificial intelligence, particularly deep learning, have introduced powerful tools capable of processing multimodal data—including physiological signals, facial expressions, and speech features—to achieve more accurate and dynamic stress detection. This review provides an in-depth analysis of deep learning models applied to classroom stress detection. The discussion covers theoretical models of stress, relevant biomarkers, traditional assessment methods, and modern architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) models, and Transformer frameworks. Furthermore, multimodal fusion strategies, performance evaluation techniques, and real-time classroom applications are examined. The paper also highlights challenges related to data privacy, scalability, and ethical considerations, while outlining future directions with emphasis on explainable AI and integration into smart classroom systems.
Kavitha et al. (Tue,) studied this question.