A Literature Review of Empirical Studies on the AI Integration in Academic Data Management Systems in Philippine Higher Education Institutions

Abstract

This literature review systematically examines empirical studies on the integration of Artificial Intelligence (AI) in Academic Data Management Systems (ADMS) within Philippine Higher Education Institutions (HEIs). As Philippine universities increasingly confront expanding student populations, complex academic records, and data-driven governance requirements, AI technologies have emerged as critical tools for enhancing efficiency, accuracy, and institutional decision-making. Drawing from peer-reviewed studies published between 2020 and 2025, this review synthesizes evidence on commonly applied AI technologies—such as predictive analytics, intelligent dashboards, learning analytics, and automated reporting systems—and evaluates their reported benefits and challenges. The review further analyzes methodological approaches used in existing empirical studies and identifies persistent limitations related to infrastructure readiness, data governance, human resource capacity, and regulatory compliance. Findings indicate that while AI adoption in Philippine HEIs demonstrates measurable improvements in academic monitoring and administrative efficiency, integration remains uneven and constrained by systemic and organizational barriers. This study provides an evidence-based framework to guide institutional planning, policy formulation, and future empirical research on AI-enabled academic data management in the Philippine higher education context.