Reframing AI-Enabled Educational Management Information Systems: A Critical Integrative Review of Governance, Institutional Readiness, and Algorithmic Ethics

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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Publication Details

Journal
International Journal of Applied Data Analytics
Published
2026-11-05
DOI
https://doi.org/10.51137/wrp.ijada.992
Primary Topic
Artificial Intelligence in Education
Type
article
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article

Reframing AI-Enabled Educational Management Information Systems: A Critical Integrative Review of Governance, Institutional Readiness, and Algorithmic Ethics

Md Redwan Laskar, Sadman Hai Nafim, Mizanul Hoque Chowdhury, Taposhi Rabeya
International Journal of Applied Data Analytics
Artificial Intelligence in Education
article

Reframing AI-Enabled Educational Management Information Systems: A Critical Integrative Review of Governance, Institutional Readiness, and Algorithmic Ethics

Md Redwan Laskar, Sadman Hai Nafim, Mizanul Hoque Chowdhury, Taposhi Rabeya
article en

Abstract

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.

International Journal of Applied Data AnalyticsVol. 1(1)
Trine University (US), International American University (LC), Westcliff University (US), International American University (US), University of La Verne (US)
Openalex Percentile: Top 9%
Artificial Intelligence in Education
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