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K. Sivakumar is an Assistant Professor in the Department of Commerce (Professional Accounting) at Erode Arts and Science College (Autonomous), Erode, Tamil Nadu. He specializes in accounting, finance, business analytics, and higher education research. His academic interests include predictive analytics, educational data mining, and student performance evaluation. He has contributed to several research publications in commerce and management studies.
This chapter provides an in-depth examination of an AI-driven predictive analytics framework for the thorough assessment of student performance and academic advancement. It stresses how important it is to combine AI, machine learning, and data-driven methods to change how schools work today. The conversation brings up important concepts, algorithms, and tech techniques 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 chapter also looks at real-time analytics, adaptive systems, and predictive modelling as examples of how these ideas might be used in the real world to improve both teaching and student interest. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical consequences. There is also talk about how new technology may affect education systems that are equipped for the future. The chapter shows how intelligent systems can fill in the holes in traditional education by providing scalable and creative solutions that make learning more effective, inclusive, and successful over time in a variety of settings.