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Artificial Intelligence (AI) is increasingly transforming educational systems by extending data-driven decision-making from classroom instruction through governance, administrative planning and system-level oversight. While AI-enabled instructional tools have been widely studied, AI integration in Educational Management Information Systems (EMIS) remains uneven and technically limited particularly in resource-constrained public and non-profit educational settings. This study provides a systematic review of the literature in order to synthesize current applications and frame AI-enabled EMIS as socio-technical governance infrastructures. Guided by the PRISMA 2020 guidelines, this critical integrative review analyzed 20 peer-reviewed studies published between January 2023 and May 2025 from Scopus and Web of Science. Implemented AI techniques, targeted administrative functions, implementation maturity levels, and system-level governance outcomes across a three-tiered analytical corpus. The synthesis identifies four key thematic areas that characterize AI-EMIS research: predictive analytics for academic and labor outcomes, system resilience in crisis situations, administrative automation and conceptual, ethical and future-oriented extensions. These findings indicate a significant gap between algorithmic capability and institutional embedding: most implementations remain confined to isolated pilots or prototypes, with few operationalized systems integrated into administrative workflows. Furthermore, non-technical barriers, including infrastructural deficits, algorithmic bias, surveillance risks, and limited stakeholder participation, continue to constrain sustainable deployment. This review moves beyond tool-centric evaluations by adopting an overarching socio-technical systems framework that integrates human-centered design principles and relational ethical perspectives, such as the African philosophy of Ubuntu. In doing so, it reframes AI-enabled EMIS from bureaucratic efficiency tools into ethically grounded, context-sensitive governance infrastructures suitable for urban and non-profit educational environments.
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Chowdhury et al. (2026) studied this question.
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