A Comprehensive Review on Literacy Rate Forecasting in Education Systems

Literacy rate forecasting plays a vital role in shaping education policies, workforce planning, and sustainable socio-economic development. With the rapid digitalization of education systems and the availability of large-scale educational data, forecasting literacy trends has evolved from traditional statistical approaches to advanced machine learning and artificial intelligence-driven models. This review paper systematically examines recent research contributions focusing on literacy rate forecasting and closely related educational prediction tasks such as student performance, enrollment trends, and educational outcomes. The study highlights the growing adoption of ensemble learning, deep learning, graph-based models, and generative AI techniques for educational forecasting. It also emphasizes the integration of explainability, interpretability, and contextual modeling in modern forecasting systems. Through a detailed analysis of twenty recent studies, this review synthesizes methodologies, advantages, and limitations, identifying key research findings and persistent challenges. The paper further discusses data quality issues, scalability constraints, ethical considerations, and policy alignment challenges. Finally, it outlines future research directions, emphasizing hybrid models, interpretable AI, and sustainable data-driven decision-making frameworks to enhance the accuracy and applicability of literacy rate forecasting in diverse education systems.