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J. Anitha Josephine experienced academicians with expertise in engineering education and interdisciplinary research. Their interests include educational innovation, sustainable development, technology integration, and applied research. They have guided numerous student projects and contributed to scholarly publications. Their work focuses on fostering innovation and skill development among learners.
This chapter provides an in-depth examination of machine learning methodologies for assessing entrepreneurial ambitions and innovation potential in young learners. It stresses the need to combine 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. To make sure that the implementation is responsible, we carefully look at problems like data quality, scalability, privacy issues, and ethical concerns. 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.