Identifying Learning Difficulties at an Early Stage in Education with the Help of Artificial Intelligence Models and Predictive Analytics

Abstract

This empirical study explores the transformative potential of integrating artificial intelligence and predictive analysis to proactively identify learning challenges in the dynamic field of education. Through the utilization of diverse data sources, advanced machine learning algorithms, and an iterative methodology to construct predictive models, the study follows a rigorous technical approach. The study underscores the negative impact of delayed identification on students' academic performance and socioemotional well-being, underscoring the critical need for early detection. By recognizing and addressing these challenges at an early stage, educators can implement targeted interventions and strategies to help students reach their full potential. It is imperative that teachers, parents, and educational professionals work together to identify and support students who may be struggling with learning difficulties. It explores Artificial-Intelligence (AI) in education, emphasizing its ability to personalize learning, streamline administrative tasks, and support language acquisition. Consideration is given to data privacy and ethical concerns, as well as the integration of predictive analysis into the classroom. The paper envisions a future where AI-driven predictive analysis fosters interdisciplinary collaboration, continuous improvement, and the incorporation of cutting-edge technologies, ultimately leads a more efficient and personalized educational experience, despite challenges such as bias mitigation and infrastructure disparities. To fully leverage the benefits of AI-driven predictive analysis in education, the conclusion focuses on the significance of ethical principles, empowering teachers, and widespread technological integration.