An IoT-Enabled Mental Health Monitoring System for English Language Students Using Generative Adversarial Network Algorithm

Abstract

In recent years, technology development has deeply impacted numerous sectors, including education. Innovations such as the Internet of Things (IoT) and Artificial Intelligence (AI) have revolutionized teaching methods, presenting personalized learning knowledge and enhancing educational results. These technologies have enabled teachers to modify lessons to specific student requirements, track progress in real-time, and provide interactive learning environments that promote engagement and retention. To address the developing educational environment these technologies allow, this paper proposed an innovative framework that integrates IoT-enabled mental health based on deep learning techniques for students of English teaching using generative adversarial networks (GANs) algorithm for personalized educational involvements. IoT devices for the entire data-gathering approach incorporate academic records and real-time mental health indices through the framework to assist educators in understanding how their students function and feel about learning. GANs handle and analyze this rather diverse data set and generate feedback and learning strategies based on students’ specific profiles. Such an integration proves to be maximally effective in increasing compliance with educational interventions while at the same time promoting the students’ all-rounded development by fulfilling their academic, emotional, and social learning requirements. The experimental results achieved superior performance with an accuracy of (0.916%), an F1 score of (0.921%), and an MCC of (0.829), and the error metrics include MAE of (0.12), MSE of (0.25), RMSE of (0.27), and MAPE of (0.31), respectively. The proposed model outperforms traditional machine learning techniques such as DNN, RNN, LSTM, and CNN, highlighting its superior predictive performance in educational mental health for English teaching applications. Moreover, the paper examines the importance of ethical considerations, educational psychology, and future research directions, emphasizing the transformative possibility of IoT and GAN technologies in proffering personalized learning methodologies in education.