Education Data & AI Readiness Diagnostic
The EDAR Index evaluates a ministry's readiness to deploy, sustain, and govern AI in education across five interdependent capability pillars — producing a composite score, a gap analysis, and a prioritised implementation roadmap.
The EDAR Index (Education Data and AI Readiness Index) is a 5-pillar, 21-area diagnostic framework that assesses an education system's capacity to safely and effectively leverage data for AI-driven decision-making. It is designed for national and sub-national education ministries, multilateral programme teams, and EdTech investors requiring a structured pre-deployment readiness baseline.
The assessment is evidence-anchored, triangulated across multiple source types, and produces outputs calibrated to government procurement, donor reporting, and ministerial decision-making requirements.
Each pillar addresses a distinct and interdependent dimension of education data and AI readiness. No system is ready for AI unless it is ready across all five.
Evaluates the completeness, accuracy, consistency, and structural soundness of core education datasets — including enrolment, assessment, teacher, and school infrastructure data. Examines data lineage, validation protocols, and the technical architecture supporting data collection and storage.
Assesses the policy frameworks, institutional mandates, and accountability mechanisms that govern how education data is collected, accessed, shared, and audited. Includes analysis of data ownership clarity, consent frameworks, and ministerial-level data governance structures.
Examines the degree to which education data systems conform to interoperability standards, support cross-system data exchange, and enable integration with multilateral reporting frameworks (UNESCO UIS, UNICEF MICS, SDG4 monitoring). Covers API infrastructure, unique identifier coherence, and data dictionary alignment.
Evaluates the specific data conditions required to deploy priority AI applications in education — including adaptive learning platforms, automated assessment tools, dropout prediction systems, and resource allocation models. Assesses feature availability, training data maturity, and algorithmic accountability mechanisms.
Assesses the human capital, change management readiness, and institutional culture required to absorb, operate, and sustain AI-driven tools at scale. Covers digital literacy baselines, technical workforce capacity, leadership alignment, and budget sustainability for data-intensive operations.
Assessment outputs are mapped to four readiness bands, each carrying specific strategic implications for planning, investment, and capacity-building priorities.
Core data infrastructure and governance foundations are incomplete or inconsistent. Significant structural investment is required before AI deployment is viable. Focus at this stage should be on data architecture remediation, governance policy development, and baseline workforce capacity.
Essential foundations are in place but significant gaps remain across one or more critical pillars. Limited, well-scoped AI pilots may be viable in specific areas. Priority action is closing identified pillar-level gaps through targeted investment and institutional capacity-building programmes.
Strong cross-pillar readiness with isolated areas requiring development. Systemic AI deployment across multiple use cases is feasible with appropriate governance guardrails. Recommended focus is on interoperability upgrades, advanced workforce training, and formal AI governance frameworks.
System demonstrates comprehensive readiness for responsible, sustained AI deployment at scale. Architecture, governance, workforce capacity, and use-case foundations are sufficiently mature. Recommended focus is on optimisation, outcome monitoring, and multi-system interoperability at the regional or global level.
Every engagement is a fixed-scope, five-week diagnostic process producing four structured deliverables for client use in planning, procurement, and accountability reporting.
A structured diagnostic report presenting composite and pillar-level scores, evidence summaries, gap analysis, and strategic observations. Calibrated for ministerial review and donor/multilateral reporting requirements.
A structured evidence register documenting all data sources, documents, and artefacts reviewed during the assessment. Provides a verifiable audit trail for governance accountability and future reassessment benchmarking.
A prioritised implementation roadmap structured across three capability-building horizons. Includes initiative-level descriptions, sequencing logic, gate milestones, and resource implication guidance.
A concise, decision-ready briefing designed for Permanent Secretary or ministerial-level audiences. Summarises findings, recommendations, and investment priorities in a format suitable for cabinet or donor presentation.