Purpose While existing studies have extensively examined AI-supported teaching and learning, limited attention has been paid to AI from an educational management perspective. This integrative conceptual review aims to synthesise recent research on the role of AI in educational management and explore its implications for educational leadership and governance. Design/methodology/approach Drawing on a structured literature search and selection process, this integrative conceptual review employs an iterative thematic synthesis to examine how AI is being applied across educational management functions. Following Torraco's (2016) framework for integrative reviews, the study prioritises theoretical development and conceptual insight over comprehensive coverage. The review analyses AI applications in strategic planning, administrative efficiency, and data-driven decision-making, whilst identifying key managerial challenges and leadership implications. Findings AI technologies offer significant potential to enhance educational management through predictive analytics, operational automation, and sophisticated data analysis. However, effective integration requires more than technological adoption; it demands strong leadership, continuous professional development, and human-centred governance frameworks. This study proposes an integrative framework conceptualising AI's role across three interrelated dimensions: (1) Management Functions and AI Applications, (2) Governance Mechanisms and Decision Rights, and (3) Leadership Capabilities. Educational leaders face substantial challenges in developing AI literacy, addressing ethical concerns about bias and privacy, navigating power dynamics with commercial vendors, and managing organisational resistance to change. Research limitations/implications As an integrative conceptual review, this study does not aim to provide exhaustive coverage of all empirical studies on AI in educational management. The synthesis is based on a selective but transparent evidence base and focuses on conceptual clarity rather than statistical generalisation. Future research should empirically test and refine the proposed framework across different institutional and national contexts. Practical implications The study identifies essential leadership competencies for the AI era: critical technology evaluation, ethical reasoning, data stewardship, collaborative leadership across diverse expertise, and adaptive learning agility. Educational institutions must establish robust governance frameworks with clear decision-rights matrices specifying who decides what and through what processes, supported by professional development strategies integrating technical understanding with ethical reflection and by formal oversight processes for high-stakes AI use. Social implications By highlighting governance, equity, and accountability concerns, this review underscores the broader societal implications of AI adoption in education. Responsible AI governance in educational institutions can help prevent the reproduction of social inequalities, protect student data, and foster public trust in AI-enabled educational systems. Originality/value This study contributes to the growing body of research on AI in education by shifting the focus toward management, leadership, and governance perspectives rather than pedagogical applications. It provides educational leaders with an original integrative framework for understanding AI's role across management functions, identifies critical competencies and governance considerations for responsible AI implementation, and offers a critical analysis of power dynamics, equity concerns, and commercialisation issues in AI deployment.
Manh Trong Nguyen (Tue,) studied this question.