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L. Sujatha is an Associate Professor in the Department of Management Studies at SRM Valliammai Engineering College, Chennai. Her expertise includes management science, organizational behavior, educational analytics, and research methodology. She has published several research articles in reputed journals. Her research focuses on student wellbeing, management education, and data-driven decision making.
This chapter provides an in-depth examination of machine learning models aimed at forecasting academic stress, anxiety, and emotional well-being among university students. It stresses the use of artificial intelligence, machine learning, and data-driven methods to change how schools work today. The conversation focuses on important frameworks, algorithms, and technological methods that make it easier to keep an eye on, forecast, and enhance student results. There is also talk about how new technology may affect education systems that are equipped for the future. In general, this chapter shows how intelligent systems can fill in the holes in traditional education by providing scalable and creative solutions that boost performance, inclusivity, and long-term academic achievement in a variety of learning settings.