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Sharmila E. M. N. is associated with the School of Education, Alagappa University, Karaikudi. Her research interests include educational innovation, entrepreneurship education, learning analytics, and student development. She actively participates in academic research aimed at enhancing teaching and learning practices. Her work focuses on integrating technology with educational advancement.
This chapter provides an in-depth examination of a predictive analytics framework utilising AI and ML to enhance student entrepreneurship and innovation skills in higher education. It stresses the need to use 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. The chapter also talks about how real-time analytics, adaptive systems, and predictive modelling can be used in real life to make teaching more successful and keep students interested. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical concerns. 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.