By Paul Atherton, Ahmad Jawad Asghar, Victoria Egbetayo, Ekua Nuama Bentil and Maria Barron
By 2050, one in three of the world’s children will live in Africa. Yet this demographic shift coincides with a profound learning crisis: over 70 percent of children in low- and middle-income countries (LMICs) cannot read and understand a simple text by age 10—and in Sub-Saharan Africa, the figure reached 86 percent before the pandemic. Without rapid acceleration in foundational learning outcomes, this demographic advantage risks becoming a source of deeper inequality and lost opportunity, talent, and productivity in the labor market.
Artificial intelligence (AI) is reshaping education, but most AI-enabled EdTech products are more responsive to high-income contexts -where infrastructure, data availability, and learning conditions are very different-while needs are greatest in LMICS. Without deliberate design and policy choices, AI risks widening the global learning gaps. This blog explores what is needed for AI-enabled EdTech to close the global learning divide and equip Africa’s next generation with the skills they will need.
AI provides an opportunity – if we build with purpose
Hundreds of AI-enabled EdTech products are being implemented in LMICs, and early-stage evidence suggests they can improve efficiency and support learning when well designed.
In Rajasthan, India, state authorities used AI-powered assessment tools to score paper worksheets of 4.5 million learners, while in Kenya, nearly 400,000 children are using EIDU, a structured pedagogy solution with demonstrated learning gains. A World Bank comprehensive after-school program in Edo, Nigeria, achieved significant learning gains after just six weeks of AI tutoring and teacher guidance.
At the same time, big tech is adding education features, such as Gemini Guided Learning, Claude’s learning mode, and OpenAI’s study mode. But context counts – if an AI tool in rural Tanzania generates a lesson plan centered on pizza instead of chapati, it’s already failed to meet learners where they are.
How can AI live up to its potential to support learning equitably and at scale?
Fab AI, the Gates Foundation, and the World Bank share a common goal: to shape the world’s best technologies to help those learning the least. Achieving this requires focusing on three priorities:
- Build equitably – AI that works everywhere
To ensure what works in high-income countries also works for LMICs, AI must be built with an understanding of local realities: languages, cultural context, curriculum, and pedagogical approaches to foundational reading and math. It must also reflect practical constraints such as infrastructure and bandwidth—highlighting the importance of low-bandwidth solutions, offline functionality, and smaller language models that can operate in resource-constrained environments.
- Work collaboratively – local developers, educators, governments, and big tech
Developers of AI-enabled EdTech around the world face many of the same challenges. The opportunity to share learning, build in the open, and avoid duplicating effort is significant – particularly in evaluation, safety, and content quality.
Only 0.2% of the data used for training AI models comes from Africa and South America. Collaboration between local developers, educators, governments, and technology companies is essential to ensure AI systems are contextually relevant, aligned with national curricula, and effective for learners in LMICs.
Joint initiatives involving educators from the outset can create safe environments for piloting new tools and enable responsible sharing of resources such as datasets and knowledge graphs. Realizing this potential requires new collaboration and governance models that deliver clear value for all partners.
Encouragingly, large scale research and skill development partnerships in LMICs are already emerging, such as Anthropic’ s partnership with the Rwandan government, Microsoft’s, initiative in Kenya and OpenAI’s accelerator in India.
Programs with a specific focus on improving foundational learning at scale in LMICs will be critical as these efforts expand.
- Build evidence and quality – AI that is safe, effective, and scalable
The World Bank, the Gates Foundation and Fab AI share a common goal of supporting countries in the responsible use of AI in education by building evidence, setting benchmarks, and scaling what works across education systems.
This requires quality checks throughout the AI product lifecycle – from early concepts and development through deployment at scale. It also means piloting AI-enabled EdTech in real-world settings and building evidence on both learning outcomes and system-level efficiency. Only then can products deliver on their intended goals.
An emerging and critical part of this quality assurance is testing AI outputs. Fab AI, with support from the Gates Foundation and the UK’s Foreign, Commonwealth and Development Office, is developing AI benchmarks, alongside conducting efficacy studies – creating a practical framework to help governments, funders, and developers distinguish promising AI-enabled EdTech from the rest. While few products currently report on evidence, there are efforts to compile evidence on the impact of AI EdTech products (see EdTech for Good, EdTech Tulna and EduEvidence). An agentic tool to compile and evaluate evidence on AI-enabled EdTech products will be available soon on Fab AI’s website.
Meanwhile, numerous World Bank-supported pilots are completed/underway across LMICs, including adaptive learning programs in Côte d’Ivoire, The Gambia and Mali; WhatsApp-based tutors in Ghana, teacher-focused solutions in Ethiopia; and youth skills programs in Tanzania and Mauritius, forthcoming. Together, these efforts are helping build the evidence needed to guide responsible adoption and scaling.
Harnessing the potential of AI to help children learn – and thrive
In November 2025, over 100 leaders from across the education and technology ecosystem including developers, governments, funders, and major technology companies convened at the AI for Education Summit in Nairobi. The goal was to focus on what it takes for AI to improve learning outcomes in Sub-Saharan Africa and beyond.
With “grounded ambition” in mind, as called for by Dr Ben Piper, Director of Global Education at the Gates Foundation, participants explored high-leverage AI use cases for teacher support, personalized learning, and assessment. Organizations across contexts are dealing with the same challenges. For AI to make a real difference in education, solutions must be systemic, grounded in local realities, and aligned across actors.
World Bank Global Education Director Luis Benveniste calls for “supporting students from foundational learning to job-relevant skills. We must leverage responsible AI to accelerate this journey and scale, guaranteeing young people can thrive in a rapidly changing world.”
Meeting this challenge requires concerted action. We invite developers, educators, governments, multilaterals, and technology companies to join us in shaping the next generation of AI-enabled EdTech – tools that are built equitably, developed collaboratively, and grounded in evidence.
Only by working together can we ensure that AI reaching classrooms is safe, effective, and designed for the realities of LMICs, helping all learners acquire the foundational skills they need to progress, access opportunity, and thrive.
