Launching our Made in Africa AI for MERL Landscape Study


The MERL Tech Initiative is pleased to launch our publication, “Made In Africa Artificial Intelligence Approaches In Monitoring, Evaluation, Research And Learning: A Practitioner Perspective And Landscape Study”. 

For the past few months, our core collaborator and Africa + AI Working Group Lead, Varaidzo Faith Magodo-Matimba, has been leading a landscape study to explore how artificial intelligence (AI) is shaping Monitoring, Evaluation, Research, and Learning (MERL) across Africa. After sharing early findings with the Africa + AI Working Group (and in our blog!), we are happy to announce that Vari is at DataFest Africa 2025 today to mark the official launch of this Landscape Study!

About the study

Based on a qualitative research design, the study engaged 23 participants from across Africa and the global MERL ecosystem, including evaluators, AI experts, civil-society leaders, and policymakers. Data were collected through key informant interviews, literature review, and practitioner consultations.

The study focuses on six questions:

  1. What knowledge and capacity gaps exist among African MERL practitioners regarding AI and data use?
  2. How can these gaps be filled through training, collaboration, or infrastructure?
  3. What does a “Made in Africa” AI approach mean in practice?
  4. How can AI enhance evidence-informed policymaking?
  5. How well do current AI tools serve African contexts?
  6. How can partnerships sustain an African-led AI and evaluation movement?

“Made In Africa Artificial Intelligence Approaches In Monitoring, Evaluation, Research And Learning: A Practitioner Perspective And Landscape Study” is available for download right now and we welcome your thoughts and feedback. 

We appreciate the Hewlett Foundation’s support and the invaluable contributions of all the community members who kindly dedicated their time to this project.

Keep an eye out on our blog: Over the next few weeks, Vari will share a series of blog posts highlighting findings and learnings from this process!

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