AI in Education Prepares Future Workforce

By 2050, one in three of the world’s children will reside in Africa. This demographic shift, however, coincides with a significant learning crisis, with over 70 percent of children in low- and middle-income countries (LMICs) unable to read and understand a simple text by age 10. In Sub-Saharan Africa, this figure reaches an even more concerning 86 percent. Without substantial improvements in foundational learning, this demographic advantage risks exacerbating inequality and limiting future opportunities.

Artificial intelligence (AI) is transforming education, but current AI-enabled EdTech products often cater to high-income contexts with vastly different infrastructure, data availability, and learning conditions. This disparity poses a risk of widening global learning gaps.

AI Implementation in Low- and Middle-Income Countries

Numerous AI-enabled EdTech products are being implemented in LMICs, showing early promise in improving efficiency and supporting learning when designed effectively. In Rajasthan, India, AI-powered assessment tools scored worksheets for 4.5 million learners. In Kenya, nearly 400,000 children utilize EIDU, a structured pedagogy solution demonstrating learning gains. A World Bank program in Nigeria achieved significant learning gains after just six weeks of AI tutoring and teacher guidance. Large technology companies are also integrating educational features into their platforms.

Priorities for Equitable AI in Education

Fab AI, the Gates Foundation, and the World Bank share a common goal: to develop AI technologies that benefit those with the greatest learning needs. Achieving this requires focusing on equitable design, collaborative development, and rigorous evidence-building.

Building Equitably

AI must be designed with an understanding of local realities, including languages, cultural context, curriculum, and pedagogical approaches. It should also account for practical constraints such as limited infrastructure and bandwidth, prioritizing low-bandwidth solutions, offline functionality, and smaller language models.

Working Collaboratively

Collaboration between local developers, educators, governments, and technology companies is crucial. Sharing knowledge, building in the open, and avoiding duplication of effort, particularly in evaluation, safety, and content quality, can accelerate progress. Only a small percentage (0.2%) of the data used to train AI models originates from Africa and South America, highlighting the need for increased local data and expertise.

Building Evidence and Quality

The World Bank, the Gates Foundation, and Fab AI are committed to supporting countries in the responsible use of AI in education by building evidence, setting benchmarks, and scaling effective solutions. This requires quality checks throughout the AI product lifecycle, including piloting in real-world settings and evaluating both learning outcomes and system-level efficiency. AI benchmarks and efficacy studies are being developed to help distinguish promising products.

Current Initiatives and Future Directions

Numerous World Bank-supported pilots are underway across LMICs, focusing on adaptive learning, WhatsApp-based tutoring, teacher-focused solutions, and youth skills programs. In November 2025, the AI for Education Summit in Nairobi brought together leaders from the education and technology sectors to explore high-leverage AI use cases.

Addressing this challenge requires concerted action from developers, educators, governments, multilaterals, and technology companies to ensure AI-enabled EdTech is safe, effective, and designed for the realities of LMICs, helping all learners acquire the foundational skills they need to thrive.