Predictive Analysis in Education: Using Artificial Intelligence Models to Identify Learning Difficulties Early

This empirical study investigates the revolutionary possibilities of integrating AI and predictive analysis to detect learning challenges at an early stage in the ever-changing educational landscape. The study uses a variety of data sources, machine learning algorithms, as well as an iterative procedure to create predictive models. It is based on a strict technical methodology. The study highlights the detrimental effects of delayed identification on students' academic alongside socioemotional well-being, emphasizing the urgent need for early detection. The function of artificial intelligence (AI) in education is examined, highlighting its capacity to personalize learning, expedite administrative duties, and assist with language learning. Carefully considered are data privacy issues, ethical issues, and integrating predictive analysis into the classroom. A future where AI-driven predictive analysis promotes interdisciplinary collaboration, continuous improvement, as well as the integration of cutting-edge technologies is envisioned by the paper, announcing a more efficient personalized educational experience, despite obstacles like bias mitigation and infrastructure disparities. In order to fully reap the benefits of AI-driven predictive analysis in education, the conclusion highlights the significance of moral principles, teacher empowerment, as well as broad technological integration.