This project focuses on creating a standardized, data-driven system to address irregular school structures that hinder effective educational policy implementation. The core idea is to bring consistency and clarity to disparities in school infrastructure, administration, and academic distribution across regions. By combining data analytics, AI-based assessment, and policy mapping, the system helps education authorities understand where gaps exist and how resources can be better utilized. The framework works like a digital evaluation assistant, analyzing real-time and historical data to support planning, budgeting, and policy decisions. It moves beyond manual reporting by offering predictive insights on policy outcomes, infrastructure needs, and funding efficiency. An interactive dashboard allows decision-makers to visualize disparities, track progress, and compare regions easily. Instead of fragmented decision-making, the approach promotes transparency, uniformity, and evidence-based governance. At its core, the methodology relies on analytical modeling and AI-driven evaluation to anticipate challenges, optimize resource allocation, and strengthen education systems at both regional and national levels.
Panchagalle et al. (2026) studied this question.