How might we enhance education by using AI to make school management and processes more efficient and effective?
How might we enhance education by using AI to make school management and processes more efficient and effective?
The conversation on AI in education has focused on classrooms and learning tools — and that’s a good thing. But we’re missing another frontier: the power of AI to make the administrative systems that support education smarter, faster, and more effective.
EdTech Hub’s AI Observatory is exploring how ministries of education can streamline bureaucracy. This means simplifying administrative procedures to ensure that information, decisions, approvals, and resource allocations move more efficiently through the system.
This week, in Issue No. 22 of the #WaypointWednesday, we spotlight predictive analytics that identify learning losses early, and algorithms supporting more equitable distribution of teachers and resources.
Early signals
Integration of AI into predictive systems to minimise learning losses
There is a global effort to address learning losses. A crucial application of AI in education management is the enhancement of early warning systems (EWS) to minimise learning losses. AI models are being integrated into national and local education systems to identify risks early and enable proactive planning.
- Philippines – Project LIGTAS (Learning Institution GeoHazard Tracking and Assessment for Safety): Project LIGTAS from the Department of Education’s Education Center for AI Research (E-CAIR) combines AI modelling, satellite data, and education metrics to predict hazards such as floods and extreme heat. By linking environmental and learning data, it enables proactive planning to maintain education continuity. (PIDS–UP Forum, 2025)
- Morocco – An AI-driven predictive modeling approach: Researchers developed an AI-based predictive model identifying students at risk of dropping out. Tested with data from the Moroccan Ministry of National Education, it demonstrated an effective model for detecting vulnerable learners. (Elbouknify et al., 2025)
AI for more equitable teacher deployment
The teacher workforce is the most important school-level determinant of learning. However, many education systems face persistent challenges in distributing resources equitably, especially teachers (World Bank, 2024). We’re seeing AI used to support more equitable teacher allocation.
- Sierra Leone – GIS-supported preference matching algorithm for teacher deployment: an ongoing EdTech Hub project with the Teacher Service Commission uses algorithms to support teacher allocation, by considering pupil-to-qualified-teacher ratios and teacher preferences. The research demonstrated that algorithm-supported deployment can increase fairness in teacher placement, promote transparency and reduce political interference. (Frazer, 2025)
- Ecuador – Quiero Ser Maestro: The Ministry of Education’s system uses a deferred acceptance algorithm and a two-stage process to match candidates to schools, allowing teachers to change their school of preference to where they have a higher chance of securing a position. This low-cost, AI-driven innovation improved individual placements and produced system-wide gains in vacancy fulfilment and the quality of teacher–school matches. (Elacqua et al., 2022; World Bank, 2024)
AI-powered mapping for improving access to education
We’re seeing AI used to improve educational access by mapping schools, identifying underserved areas, and guiding investments in connectivity and resources.
- Global – Giga Initiative: The Giga Initiative by UNICEF and the International Telecommunication Union (ITU) uses AI and geospatial data to map school locations and highlight connectivity gaps. Its dashboards help governments target investment in last-mile internet and power access. (AI for Good, 2024)
- Philippines – Project PAARAL: The Department of Education’s Education Center for AI Research (E-CAIR) uses AI built on open-source location and road data to improve its Senior High School Voucher Programme. The system maps school accessibility to identify underserved areas and funding shortfalls that limit access to private education for low-income students. (PIDS–UP Forum, June 2025)
Reflections:
- To ensure accountability and stakeholder buy-in, decision-makers should be transparent and provide clear explanations of how and why AI algorithms or tools are being used. In particular, decisions that affect the lives of those involved in the system, such as teacher allocation or decisions around students’ educational futures, should have robust accountability and transparency mechanisms due to their significant human impact. (D’Rozario, 2025)
- While centralised algorithm-supported deployment brings in transparent, uniformly applied rules, the outputs are only as good as the (quality of) data. School-level actors hold rich, local knowledge of school needs intricate human factors such as language barriers, cultural differences, or personal preferences that may affect teacher effectiveness in a particular school. Thus, centralised deployment needs to be coupled with localised human insights for more effective deployment and teacher satisfaction.
- Early Warning Systems can carry risks when not designed and used carefully. Systems trained on historical data can reproduce structural inequities related to gender, disability, language, or location, leading marginalised learners to be disproportionately labeled as “at risk.” Such labeling can unconsciously lower teachers’ expectations and shift focus to students’ deficits rather than strengths. Moreover, constant monitoring and warnings can provoke anxiety, disengagement, or resistance, turning a tool intended to support learners into one that deepens disadvantage.
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We’d love to hear from you! What’s been shaping your thinking on AI? Drop your thoughts (and reading recommendations) in the comments. Explore more from EdTech Hub’s AI Observatory.
EdTech Hub’s AI Observatory is made possible with the support of the UK’s Foreign, Commonwealth and Development Office.
