AI-ASSISTED DATA-DRIVEN DECISION-MAKING IN SCHOOL MANAGEMENT: A MODEL FOR DEVELOPING SCHOOL LEADERS' MANAGERIAL DIGITAL COMPETENCE

The rapid spread of artificial intelligence (AI) in education is changing the informational basis of school management and expanding the range of decisions that can be supported by data analytics, predictive models and generative systems. The aim of this study is to substantiate a model for developing school leaders’ managerial digital competence for AI-assisted, data-driven decision-making. The study applies qualitative document analysis and integrative thematic synthesis to international policy frameworks, national strategic documents and recent peer-reviewed research on digital school leadership, AI governance and educational decision support. The analytical procedure focused on four categories: managerial functions enhanced by AI, competencies required from school leaders, risks associated with algorithmic decision support, and conditions for professional development. The results identify six interrelated components of managerial digital competence: data and AI literacy, analytical decision competence, strategic digital planning, organizational change leadership, ethical-algorithmic governance, and human-centred communication. A cyclical decision model is proposed in which AI supports data aggregation, pattern detection, forecasting and scenario generation, while the school leader retains responsibility for interpretation, stakeholder deliberation, final judgment and monitoring. Three developmental levels—operational, analytical and strategic-transformational—are also defined. The findings indicate that AI competence for school leaders should not be reduced to technical tool use; it must combine evidence-based management, ethical oversight, organizational learning and professional responsibility. The proposed model can inform leadership training, school digital strategies and institutional standards for responsible AI adoption.