AI-Driven Educational Equity: A Data Architecture for Addressing Achievement Gaps in Public Education Systems

Abstract

Educational inequity persists as a critical societal challenge, with significant achievement gaps across socioeconomic and racial demographics. Traditional educational systems operate in fragmented environments, limiting the effective utilization of data for timely interventions. This article presents a comprehensive cloud-native, AI-powered education analytics platform designed to address these educational disparities through advanced data integration and predictive modeling. The proposed architecture consists of five interconnected layers: ingestion, data lakehouse core, AI/ML analytics, serving, and governance. By unifying disparate data sources, the platform enables early identification of at-risk students, optimized resource allocation, and personalized learning interventions. The ingestion layer connects with various educational information systems, creating comprehensive student profiles while reducing manual reporting burdens. The data lakehouse core provides a scalable foundation for longitudinal analysis while ensuring regulatory compliance. Machine learning models achieve superior accuracy in predicting student outcomes and resource impact, while fairness monitoring safeguards against algorithmic bias. Visualization interfaces translate complex analytics into actionable insights for teachers, administrators, and policy stakeholders, significantly improving decision-making efficiency. The governance layer ensures comprehensive data protection through multilayered security measures and transparent AI explanations, fostering stakeholder trust while maintaining compliance with educational privacy regulations. This integrated approach demonstrates substantial potential for reducing achievement gaps and advancing educational equity.