Harnessing Artificial Intelligence in School Management: A Systematic Review of Applications, Challenges, and Future Directions

This systematic review examines artificial intelligence (AI) applications in K–12 school management, with a primary focus on administrative efficiency, organisational decision-making, leadership functions, and data-driven governance. It identifies critical challenges, including data privacy risks, infrastructural inequities, and ethical governance gaps, to provide actionable insights for equitable AI integration. Adhering to PRISMA guidelines, we analysed 16 peer-reviewed studies (2020–2024) sourced from Scopus, Web of Science, and IEEE Xplore. A three-stage coding process (open, axial, selective) categorised 2,143 data points, supported by the Mixed Methods Appraisal Tool (MMAT) for quality assessment. Inter-rater reliability was robust (Cohen’s κ = 0.82–0.85), and findings were validated through expert feedback and cross-referencing with seminal works. The synthesis yielded six thematic functions of AI in school management: managerial efficiency, strategic decision-making, educational facilitation, resource optimisation and security, monitoring and evaluation, and communication and transparency. Within the reviewed studies, individual investigations reported efficiency gains such as automation of 34% of administrative tasks and reductions in teacher workload by 11.2 hours weekly in well-resourced settings; early warning systems were associated with an 18% decline in rural dropout rates in specific contexts. These figures reflect findings from individual studies included in the corpus rather than pooled estimates of the review as a whole. Separately, background literature indicates that 63% of Sub-Saharan African schools lack reliable internet connectivity, that certain student-placement algorithms achieve 92% accuracy while embedding socioeconomic biases, and that 78% of schools operate without dedicated AI ethics committees. Whereas prior reviews have concentrated largely on the technical functions of AI or its learner-facing applications, this study fills a gap by examining AI through the lens of school management, organisational change, and educational leadership. It introduces a tiered implementation framework aligned with institutional maturity levels and a global competency matrix for AI-ready leadership, both of which are grounded in management theory and address unresolved questions about how schools can govern AI adoption systematically and equitably. These contributions are especially pressing given that 84% of OECD nations have embedded AI integration in national education strategies.