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Roseline Jesudas is an academician and researcher affiliated with Northern Border University, Kingdom of Saudi Arabia. Her research interests include interdisciplinary studies in social sciences, education, technology-enabled learning, and community development. She has contributed to various scholarly publications and is actively involved in teaching, research, and academic mentoring, with a focus on promoting innovative and impactful research in higher education.
This chapter offers an in-depth examination of deep learning-driven adaptive learning models aimed at personalised education and the advancement of academic growth. It stresses the importance of using artificial intelligence, machine learning, and data-driven methods to change how schools work today. The debate brings up important frameworks, algorithms, and technological methods that make it easier to keep an eye on, forecast, and enhance student-related results. The suggested methods support personalised learning, quick intervention, and smart academic decision-making by using big datasets and smart analytics. The chapter also looks at practical uses, such as real-time analytics, adaptive systems, and predictive modelling, that make teaching more successful and keep students interested. 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.