Nigeria’s 2028 Education Data Target: What Has Been Built, and What Is Still Missing?

Nigeria’s 2028 Education Data Target: What Has Been Built, and What Is Still Missing?

Nigeria has launched a national education data platform and introduced unique learner identifiers. But how much of the infrastructure needed for real-time data exchange is operational, and what remains to be demonstrated before 2028?

By 2028, Nigeria intends to establish an education data system that allows all states to exchange priority education datasets with federal systems, enabling real-time, evidence-based decision-making and automated resource allocation using verified information.

This is the vision outlined under the Data, Education Management Information System (EMIS), Security and Education Management pillar of Nigeria's National EdTech Strategy (2025–2030).

The proposed infrastructure includes the Nigeria Education Data Infrastructure (NEDI), standardised Application Programming Interfaces (APIs), state-level data exchange mechanisms, identity verification through the National Identity Management Commission (NIMC), and a National Education Data Trust Framework to regulate information sharing and accountability.

The objective is to move education management away from fragmented records towards a system where reliable information can inform decisions about school funding, teacher deployment, infrastructure development, and other educational needs.

However, two years before the 2028 target, the relevant question is no longer simply what Nigeria intends to build. It is what has already been implemented, what the available evidence demonstrates, and which capabilities still need to be established.

From Policy Commitments to Operational Infrastructure

Nigeria is not starting from scratch.

The Federal Ministry of Education has maintained the Nigeria Education Management Information System (NEMIS) and coordinated the Annual School Census for several years. These systems collect information on school enrolment, teachers, classrooms, and other educational indicators.

However, the previous census process relied heavily on paper-based reporting. According to the DHIS2 implementation team, compiling and validating the information could take close to two years.

This presented an obvious limitation for educational planning. A system that takes almost two years to process information cannot consistently provide the timely evidence required to respond to changing school needs.

In January 2024, the Ministry began piloting a digital EMIS in 12 states, supported by UNICEF Nigeria, HISP Nigeria, and the HISP Centre at the University of Oslo.

The system was built using DHIS2, an open-source information management platform that supports data collection, validation, analysis, and reporting.

On 1 July 2026, the Federal Ministry of Education officially launched the Digital National Education Management Information System (DNEMIS) nationwide.

According to the DHIS2 implementation report published in August 2026, the platform had captured records from 124,548 schools, representing 58.4% national coverage. The Ministry also reported information relating to more than 40 million learners, approximately 1.19 million teachers, 730,000 classrooms, and 607,000 sanitation facilities.

These figures establish that Nigeria has moved beyond the announcement stage to the deployment of a national digital education reporting platform.

The underlying technology is also identifiable. DNEMIS uses DHIS2's aggregate reporting and Tracker capabilities, including modules for learner and teacher records, attendance monitoring, offline mobile data collection, built-in validation, and dashboards accessible at different administrative levels.

This matters because the platform is not merely a proposed database. There is documented evidence of its deployment, technical functionality, implementation partners, and national rollout.

However, the availability of these capabilities does not automatically establish that every state and institution is exchanging verified datasets with federal systems in real time.

That distinction is central to assessing progress towards 2028.

The Data Coverage Question

One of the first issues worth examining is the completeness of the information captured within the national system.

At the July 2026 launch, the Ministry reported 58.4% school coverage, based on records from 124,548 schools.

The DHIS2 implementation profile also reports that the 2024/2025 Annual School Census achieved an 81.7% reporting rate among public basic education schools.

These figures should not be treated as conflicting measurements of the same population. The 81.7% figure concerns public basic education schools, while the broader national coverage figure includes a different reporting population.

They show why reporting denominators must be clearly defined. The August DHIS2 article also recounts an earlier conference statement citing 204,048 schools and 32 million learners, compared with 124,548 schools and more than 40 million learners at the July launch. Without aligned definitions and reporting dates, these figures should not be combined or interpreted as a simple trend.

A national platform may be available across all 36 states and the Federal Capital Territory without having complete and validated records from every school.

Similarly, the number of learners stored within a system does not, on its own, establish that their individual records are accurate, current, or free from duplication.

The implications become more important when data are expected to influence resource allocation.

For example, if school infrastructure funding is informed by available classroom data, institutions with incomplete or outdated records may be poorly represented in the allocation process.

This does not prove that such an outcome is currently occurring. Rather, it illustrates why data completeness, validation, and regular updating are necessary safeguards before automated decisions are introduced.

For the 2028 target, public reporting should therefore go beyond the total number of records collected. It should also disclose how frequently records are updated, what proportion passes validation checks, and how missing or inconsistent information is addressed.

Nigeria Already Has Identity-Linked Education Records

Another relevant development is the existing use of digital identity verification by Nigerian examination bodies.

The Joint Admissions and Matriculation Board (JAMB) requires candidates registering for the Unified Tertiary Matriculation Examination (UTME) to provide their National Identification Number (NIN). Its registration process also involves biometric verification, linking candidates to identifiable personal records.

Similarly, the National Examinations Council (NECO) requires candidates registering for the 2026 Senior School Certificate Examination to validate their NIN and other personal details. Its guidelines also require biometric fingerprint capture before registration records can be uploaded.

These systems demonstrate that identity-linked record management already exists within important parts of Nigeria's education sector.

A further development occurred on 9 April 2026, when the Federal Ministry of Education announced the nationwide introduction of the Learner Identification Number (LIN).

The Ministry reported that more than 1.9 million candidates registered for the 2026 WAEC and NECO examinations had already been issued unique learner identification numbers. Ministry documents also use the terms Learners Identification Number (LIN), National Learner Identity Number (NLIN), and Examination Learners’ Identity Number; published technical specifications should clarify how these labels relate to one another.

The LIN is intended to provide a consistent academic identity that supports learner tracking across schools and educational transitions.

This initiative is relevant to the National EdTech Strategy because reliable learner identification is an important foundation for integrating records across institutions.

However, identification and interoperability are not the same thing.

JAMB may hold a verified candidate registration record, while NECO maintains examination information and schools maintain enrolment and continuous assessment records. For these systems to support a unified education database, records must be linked through consistent identifiers, shared data definitions, and appropriate access controls.

The Ministry has also introduced measures to standardise continuous assessment submissions across examination bodies, creating a potential pathway for more consistent educational records.

What remains to be demonstrated through publicly accessible technical documentation is the extent to which these systems currently exchange learner-level information with DNEMIS or other federal education data infrastructure.

A national learner identifier represents progress in establishing a common reference for records. Its broader value depends on the systems, institutions, and governance arrangements that make consistent record exchange possible.

Interoperability Is More Than Connecting Databases

The National EdTech Strategy proposes a central data exchange infrastructure supported by standardised APIs, state integration mechanisms, and secure access for authorised institutions.

In technical terms, APIs allow different software applications to exchange information according to agreed rules.

However, providing an API does not necessarily make the information exchanged consistent or reliable.

Consider a situation where two states report student enrolment differently. One might include learners who registered at the beginning of the academic year, while another might report only those actively attending school during the reporting period.

Both datasets could be transmitted successfully to a federal platform, but combining them without harmonising their definitions could produce misleading comparisons.

Meaningful interoperability therefore requires more than technical connectivity. It requires common definitions, standardised school and learner identifiers, consistent reporting periods, documented validation procedures, and clear rules governing how information is exchanged.

The proposed state integration layer is particularly important in this regard. Its intended purpose is to connect existing state systems to the national infrastructure without requiring every institution to operate identical software.

The important implementation questions are whether these interfaces have been deployed, which states are using them, and whether their exchanges can be independently verified.

Although the DHIS2 implementation record establishes nationwide deployment of DNEMIS, it does not, by itself, demonstrate that the full set of API-based, cross-institutional exchanges envisaged by the strategy is operational.

The distinction should remain clear: a shared national reporting platform is one form of integration, while interoperable exchange between independently managed systems is another.

Data Security and Governance Remain Essential

The strategy also proposes integrating education records with NIMC and establishing a National Education Data Trust Framework to regulate access, information exchange, and accountability.

The proposed framework includes federated identity management, permission-based access, and automated audit trails.

These mechanisms could improve accountability by helping institutions control who accesses data and maintain records of how information is used.

However, the existence of a proposed security architecture is not evidence that every governance and access-control mechanism has been implemented.

Nigeria already has a legal foundation through the Nigeria Data Protection Act 2023, which establishes requirements for lawful processing, information security, and the protection of personal data, including children's information.

An integrated national education database would therefore need to demonstrate how these obligations are applied in practice.

This includes defining which organisations can access particular records, the purposes for which information may be shared, how inaccurate records can be corrected, and what safeguards exist against unauthorised access.

It also raises questions about identity verification and inclusion.

For instance, how would the system maintain accurate educational records for learners who do not yet have complete national identity documentation?

The ability to link education records to national identity systems has potential administrative benefits. However, implementation must account for learners whose documentation is incomplete, particularly where identity requirements could affect access to educational services.

These are not peripheral concerns. Data governance forms part of the infrastructure required for a secure and reliable national information system.

Can the Infrastructure Support Trustworthy AI?

Nigeria’s Ministry of Education has described NEDI as an AI-enabled national data infrastructure. That ambition raises a separate question from whether DNEMIS can collect records: are the records suitable for trustworthy predictive and algorithm-assisted decisions? An AI-ready system requires documented data provenance, consistent definitions, representative coverage, evaluation protocols, and human accountability. An AI-enabled description is not proof that a specific predictive model is in production or that its outcomes have been independently assessed.

Consider three potential uses. First, anomaly-detection models could flag improbable enrolment changes, duplicate records, or inconsistent classroom counts for human review. Second, predictive models might help identify schools at risk of overcrowding or learners who need additional support. Third, decision-support systems could help administrators compare infrastructure and teacher-allocation scenarios. These are possible applications, not verified deployments within DNEMIS.

Each use introduces risks. Schools with incomplete records could be underrepresented in model training and consequently receive less attention. A model trained on historical funding patterns might reproduce existing geographic disparities. A technically accurate prediction might still be unsuitable for high-stakes decisions if the underlying features are outdated, its error rates differ across communities, or affected institutions cannot challenge the result.

Before adopting such systems, policymakers should publish the intended use, input datasets, validation results, subgroup performance, human oversight procedures, and an appeal or correction pathway. Especially where children’s data are involved, privacy-preserving access, data minimisation, and purpose limitation should be designed into the architecture rather than added after deployment.

What Would Demonstrate Progress Towards 2028?

Based on the strategy's stated objectives and the developments documented so far, several capabilities would provide clearer evidence of progress.

The first is demonstrable state-to-federal interoperability. Beyond confirming that DNEMIS has been deployed nationally, the Ministry could publish information showing which states are exchanging standardised datasets, the frequency of those exchanges, and the types of records involved.

The second is measurable data quality. National reporting should identify the completeness, accuracy, timeliness, and validation status of submitted datasets, rather than relying exclusively on aggregate record counts.

The third is documented integration between existing education systems. This includes showing how learner identification records, school-level information, continuous assessment data, and examination records can be linked without unnecessary duplication or inconsistent definitions.

The fourth is an operational governance framework. Published data-sharing protocols, clearly assigned institutional responsibilities, access-control arrangements, and compliance mechanisms would help demonstrate that the proposed security requirements are being translated into practice.

Finally, there needs to be evidence of data utilisation.

The 2028 vision does not stop at data collection or exchange. It explicitly includes evidence-based decision-making and automated resource allocation.

Consequently, an important measure of implementation would be whether verified education datasets are being used to inform actual administrative decisions, such as school infrastructure investment, teacher deployment, or targeted support for underserved communities.

If automated resource allocation is introduced, its decision rules, validation requirements, exception procedures, and accountability mechanisms should also be documented.

These are practical indicators against which progress can be assessed without assuming that the entire system must already be fully operational.

What the Evidence Establishes—and What It Does Not

Nigeria's education data reforms have moved beyond policy formulation.

The nationwide launch of DNEMIS, the use of DHIS2 for education data management, existing examination registration systems, and the introduction of the Learner Identification Number provide documented evidence of developing digital infrastructure.

However, the 2028 vision is broader than any individual platform or identification system.

It requires dependable data exchange between federal and state institutions, consistent standards, reliable verification, secure information governance, and mechanisms for translating educational records into administrative decisions.

The public evidence reviewed establishes progress in platform deployment, digital data collection, and learner identification. It provides less conclusive evidence about the operational status of the full cross-system architecture and automated resource allocation mechanisms described in the strategy.

That is not evidence that these capabilities do not exist. It means that their implementation cannot be fully established from the documentation examined.

As Nigeria approaches 2028, clearer public reporting on these specific capabilities would make it possible to assess progress against the government's own commitments.

The central issue is no longer whether Nigeria can collect education data digitally. It is whether the systems collecting those data can exchange them reliably, verify their accuracy, protect the individuals represented, and produce information that improves educational decisions.