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M. Bhuvaneswari serves as an Assistant Professor in the Department of Computer Science and Engineering at Arjun College of Technology, Coimbatore. Her areas of interest include machine learning, software engineering, data science, and educational computing. She has guided student projects and participated in research activities related to intelligent systems. Her work focuses on practical applications of AI in education and society.
This chapter offers an exhaustive examination of machine learning methodologies for the early identification and forecasting of student stress and cognitive-behavioral alterations. It stresses how important it is to use artificial intelligence, machine learning, and data-driven methods together to change how schools work today. The talk focuses on important frameworks, algorithms, and technology methods that make it easier to keep an eye on, forecast, and enhance student results. The suggested methods support personalised learning, timely intervention, and smart academic decision-making by using massive datasets and smart analytics. The part that new technologies play in making education systems suitable for the future is also spoken about. The chapter shows how intelligent systems can fill in the gaps in traditional education by providing scalable and creative solutions that boost performance, inclusivity, and long-term academic achievement in a variety of learning settings.