Integrating Artificial Intelligence and Data-Driven Decision Making to Enhance Educational Administration in the Digital Era

Schools and other educational institutions hold large amounts of information about attendance, achievement, teaching, staffing, budgets, and student support. Yet having more data does not necessarily lead to better decisions. Records may be scattered across systems, staff may lack confidence in interpreting them, and useful findings may never translate into action. Artificial intelligence (AI) offers new ways to identify patterns, estimate risks, summarize records, and compare administrative options. Drawing on scholarship about data-driven decision making (DDDM), educational information systems, school leadership, and responsible AI, this article proposes a framework for AI-assisted school administration. It links data readiness, AI-supported analysis, professional interpretation, administrative action, and evaluation, with human oversight and governance running through every stage. Examples involving student support, instructional planning, staffing, and budgets show how the approach might work in practice. The article also considers ways to assess decision quality and addresses privacy, bias, reliability, organizational capacity, and unequal access. As a conceptual proposal, the framework still requires testing in real school settings. Its central argument is that AI can strengthen administrative judgment, but should not replace the people responsible for consequential educational decisions.