AI for MERL in Africa
Made in Africa AI for MERL: A Case Study for Evaluators
A practical EvalCommunity Academy case study for evaluators, M&E specialists, researchers, policymakers, and development practitioners working on artificial intelligence, evidence systems, decolonial evaluation, and responsible technology adoption in African contexts.
Last updated: May 2026 · 9 min read · EvalCommunity Academy case study
Introduction
Made in Africa AI for Monitoring, Evaluation, Research, and Learning refers to the development, selection, governance, and use of artificial intelligence in ways that are rooted in African contexts, languages, data, knowledge systems, and community priorities. For evaluators, the question is not only whether an AI tool can summarize data or automate analysis. The deeper question is whether the tool supports local agency, protects communities, strengthens evidence use, and avoids reproducing extractive or externally imposed systems of knowledge.
This EvalCommunity Academy case study is based on the report Made in Africa Artificial Intelligence for Monitoring, Evaluation, Research and Learning: A Practitioner Perspective and Landscape Study by Varaidzo Magodo-Matimba for The MERL Tech Initiative. The report examines whether and how AI might serve African MERL systems, drawing on qualitative interviews with 23 AI experts, technologists, evaluators, civil society leaders, policymakers, and practitioners from across Africa and the global MERL ecosystem.
The report argues that AI adoption in African MERL should not be treated as inevitable or automatically beneficial. Instead, practitioners should ask whether AI is appropriate, who controls the data, whose languages are represented, whose knowledge counts, and whether communities benefit from the systems being developed.
Quick Answer
AI can support MERL in African contexts only when it is evaluated as a socio-technical, political, linguistic, and ethical system. Evaluators should assess whether AI tools are locally relevant, culturally responsive, community-governed, language-inclusive, environmentally responsible, and aligned with African-defined evidence priorities.
Key Takeaways
- AI for MERL should be evaluated beyond technical performance, including governance, ownership, language inclusion, ethics, and long-term community value.
- Current AI tools often fail to surface African research, African languages, and African evaluation frameworks.
- The main capacity gap is not only practitioner skills; it is also the lack of indigenous data, contextual nuance, and African epistemologies in dominant AI systems.
- African language exclusion creates evidence gaps because communities become less visible when their languages are absent from AI datasets.
- AI adoption can reinforce extractive systems through data extraction, conflict mineral supply chains, energy use, and commercialization of African knowledge by external actors.
- A Made in Africa approach requires community ownership, participatory design, African language inclusion, locally controlled infrastructure, and Ubuntu-informed ethics.
Table of Contents
Case Background
The African MERL ecosystem is changing at the same time that artificial intelligence is rapidly reshaping evidence generation, knowledge production, and decision-making. The report describes this as a pivotal moment where three forces intersect: the Made in Africa Evaluation movement, rapid technological development including AI, and expanding digital infrastructure across the continent.
The study builds on the Made in Africa Evaluation movement, which challenges donor-driven, Western-centered evaluation frameworks and emphasizes African epistemologies, community ownership, relational knowledge systems, indigenous languages, and locally meaningful definitions of success.
The report also highlights a growing AI governance landscape in Africa. It references the African Union’s Continental AI Strategy and national AI strategies or policy frameworks in several countries, while noting that policy progress does not automatically translate into implementation capacity, infrastructure, funding, or community benefit.
The Evaluation Problem
The central evaluation problem is that AI tools may appear efficient while remaining poorly aligned with African MERL realities. A tool may summarize reports, translate text, or analyze datasets, but still reproduce Western assumptions, ignore African languages, miss local research, or undermine community control over evidence systems.
For evaluators and M&E professionals, this means AI should not be assessed only through accuracy, speed, or cost savings. It should also be assessed through questions of agency, epistemic justice, language inclusion, data sovereignty, environmental harm, institutional readiness, and accountability to the communities whose lives are represented in the data.
Core evaluation question: Does the AI system genuinely serve African communities, or does it mainly extend external models, external markets, and external definitions of evidence?
Main Findings for Evaluators
1. Current AI tools often misrepresent or miss African evidence
Practitioners reported that AI tools often fail to retrieve Africa-relevant literature, frameworks, or contextual evidence, even when users specifically ask for African examples. This creates a major risk for evaluation practice because AI-generated outputs may appear authoritative while excluding relevant local knowledge.
2. African language exclusion is an evidence-system problem
The report identifies weak African language resources as a major constraint for AI-enabled MERL. When African languages are absent from AI datasets, speakers of those languages become less visible in products, services, analysis, and policy evidence. This is especially important in sectors such as health, education, agriculture, and social protection, where local language and cultural meaning affect interpretation.
3. Capacity gaps are systemic, not only individual
The report challenges the assumption that African practitioners simply need to learn how to use Global North AI tools better. Instead, it argues that dominant AI systems themselves lack African data, nuance, languages, and epistemological foundations. Capacity development should therefore include practitioner training, but also institutional transformation, local AI development, better data governance, and collaboration between AI specialists and MERL practitioners.
4. AI governance must protect communities and enable African-led innovation
The report notes that many AI policies focus on risk protection, including privacy, surveillance, consumer rights, and data protection. These protections are necessary, but not sufficient. African AI governance also needs to support community-driven innovation, local research, public-interest infrastructure, funding for small innovators, and procurement pathways for locally accountable AI systems.
5. AI adoption can reproduce extractive systems
The report places AI within wider global supply chains and power relations. It raises concerns about conflict minerals, data extraction, labor exploitation, energy-intensive data centers, and the commercialization of African data by external actors. For evaluators, this means AI evaluation should include environmental justice, human rights, data ownership, and benefit-sharing questions.
6. A Made in Africa approach requires participation and ownership
A Made in Africa approach is not simply an AI product built on the continent. It is an approach that is accountable to African communities, shaped by African practitioners, responsive to local languages and contexts, and grounded in African values such as Ubuntu, relationality, collective benefit, and social justice.
Evaluation Framework for AI in African MERL
EvalCommunity Academy users can adapt the following framework when evaluating AI tools, pilots, platforms, or policies in African MERL contexts.
Relevance and necessity questions
- Is AI necessary, or would a simpler, more accountable digital or non-digital approach be more appropriate?
- What MERL problem is the AI tool meant to solve?
- Who defined the problem, and who benefits if it is solved?
- Does the tool support locally defined outcomes and priorities?
- Can communities reject or reshape the tool if it does not serve their needs?
Data and language questions
- Which African languages are included, and which are excluded?
- Are datasets representative of rural communities, women, youth, elders, persons with disabilities, and marginalized groups?
- Who owns the data used to train or operate the system?
- Are there community-led data governance or benefit-sharing mechanisms?
- Does the system preserve cultural meaning, or does it flatten local concepts into colonial or external categories?
Ethics, power, and accountability questions
- Who is accountable if the AI system produces harmful, biased, or misleading evidence?
- Does the system reinforce Western evaluation frameworks at the expense of African epistemologies?
- Are affected communities involved in design, testing, interpretation, and governance?
- Does the tool make evidence production more equitable, or does it increase dependency on external actors?
- Are environmental, labor, and human rights risks included in the evaluation?
Institutional readiness questions
- Does the organization have the infrastructure, skills, funding, and governance arrangements needed to use the tool responsibly?
- Are AI and MERL specialists working together, or are they operating in separate silos?
- Is there a plan for training, maintenance, documentation, and long-term ownership?
- Can local institutions modify or audit the system?
- Is adoption driven by local demand or by donor, vendor, or market pressure?
Practical Lessons for M&E and Development Professionals
First, evaluate AI as part of a wider evidence ecosystem. AI tools do not only process information; they shape what counts as evidence, whose knowledge becomes visible, and which decisions appear justified.
Second, treat language inclusion as a core evaluation criterion. African language exclusion is not a minor usability issue. It affects representation, service delivery, evidence quality, and policy relevance.
Third, avoid equating adoption with progress. The report cautions against narratives that frame Africa as behind and therefore in need of rapid AI adoption. Responsible evaluation should ask whether AI should be adopted at all, and under what conditions.
Fourth, include community agency and data sovereignty in evaluation design. Communities should not only provide data; they should shape how data is collected, interpreted, governed, and used.
Finally, evaluate the full cost of AI. This includes not only software and training costs, but also environmental costs, labor conditions, infrastructure dependencies, data ownership, and long-term governance responsibilities.
Download the Original Report
Keep the original report as a reference for AI evaluation design, MERL training, policy analysis, responsible data governance, and Made in Africa Evaluation discussions.
Useful External Resources
- The MERL Tech Initiative report page for the source publication and related context.
- UNESCO Recommendation on the Ethics of Artificial Intelligence for responsible AI principles.
- OECD AI Principles for trustworthy and human-centered AI governance.
- African Union for continental policy and governance context.
- Masakhane for African natural language processing and language inclusion work.
FAQ
What is Made in Africa AI for MERL?
Made in Africa AI for MERL is an approach to artificial intelligence that is shaped by African practitioners, communities, languages, data, values, and evidence priorities. It emphasizes community ownership, participatory design, African epistemologies, and local accountability.
Why should evaluators be cautious about AI adoption?
Evaluators should be cautious because AI tools can reproduce bias, exclude African languages, miss local research, reinforce external evaluation frameworks, and create dependency on systems controlled by external actors.
What are the main capacity gaps identified in the report?
The report identifies technical, ethical, linguistic, cultural, institutional, and infrastructure-related capacity gaps. It also reframes the issue by arguing that many dominant AI systems lack the African data and contextual sophistication needed for meaningful MERL work.
Why do African languages matter for AI-enabled evaluation?
African languages matter because they carry local knowledge, meaning, and community experience. If languages are excluded from AI datasets, the people who speak those languages may also be excluded from evidence systems and decision-making.
What should evaluators examine before recommending AI?
Evaluators should examine whether AI is necessary, whose data is used, which languages are included, who governs the system, who benefits, what harms may occur, and whether the tool strengthens or weakens community agency.
Can AI support African MERL practice?
Yes, but only under the right conditions. AI may support data analysis, translation, monitoring, synthesis, and learning, but it must be locally relevant, ethically governed, language-inclusive, community-accountable, and aligned with African-defined priorities.
Conclusion
This case study shows that Made in Africa AI for MERL is not mainly a technical agenda. It is an evaluation, governance, language, ethics, and power agenda. The report challenges practitioners to move beyond asking how Africa can adopt AI and instead ask what technologies genuinely serve African communities.
For EvalCommunity Academy users, the main lesson is practical: evaluate AI tools through their full social and institutional context. A responsible evaluation should ask whether AI strengthens local ownership, protects communities, includes African languages, supports African knowledge systems, and creates public value without reproducing extractive patterns.
Interpretation and analysis by EvalCommunity: This case study synthesizes findings from Made in Africa Artificial Intelligence for Monitoring, Evaluation, Research and Learning: A Practitioner Perspective and Landscape Study by Varaidzo Magodo-Matimba, published by The MERL Tech Initiative. All direct quotes and metrics are from the original sources. We encourage readers to consult the original documents for complete context.
