AI and Big Data in Private School Decision-Making: A Human-Centered Governance Framework

Abstract

This article examines how artificial intelligence and big data can support decision-making in private school management while preserving educational judgment, stakeholder trust, and institutional responsibility. Private schools operate under distinctive pressures, including enrolment competition, parental choice, financial sustainability, reputational risk, and regulatory compliance. These conditions make data-driven tools attractive for admissions forecasting, learning quality monitoring, student support, teacher development, financial planning, family communication, reputation analysis, and risk management. However, the use of AI in private schools also raises significant concerns, including data privacy, algorithmic bias, excessive surveillance, metric drift, vendor dependence, and the possible substitution of professional judgment with automated recommendations. Drawing on literature from AI in education, learning analytics, privatization, and data ethics, this article argues that AI should be understood as a socio-technical system rather than a neutral technical upgrade. It proposes a human-centered governance framework based on educational purpose, proportionality, contestability, transparency, stakeholder participation, vendor accountability, professional development, and continuous audit. The article concludes that AI and big data can improve private school decision-making only when they are embedded in responsible governance practices that strengthen, rather than weaken, educational values and institutional legitimacy.