Loading...
Archit Goel is an Assistant Professor in the Department of CSE (Data Science) at Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India. His areas of expertise include data science, artificial intelligence, social media analytics, and machine learning applications.
The swift expansion of social media platforms has profoundly affected student behaviour, frequently resulting in excessive use and potential addiction that detrimentally effects academic achievement and mental health. This chapter examines the detection and study of student social media addiction with AI, employing machine learning, deep learning, and behavioural analytics. Utilising data from digital activity logs, usage patterns, sentiment analysis, and psychological markers, AI models can detect early manifestations of addictive behaviour and evaluate its intensity. Applications in educational settings, mental health surveillance, and tailored intervention systems are emphasised. Data privacy, ethical considerations, and model bias are rigorously scrutinised. Emerging themes such as multimodal data analysis, explainable AI, and real-time monitoring systems are also examined. By employing AI-driven strategies, schools can proactively mitigate social media addiction, promote student well-being, and improve academic performance.