In just six weeks, secondary school students in Edo, Nigeria participating in an after-school program combining AI tutoring with teacher guidance, achieved learning gains of 0.31 standard deviations, equivalent to roughly 1.5–2 years of typical schooling. Behind these results was a thoughtfully designed intervention that placed teachers and students at the center, harnessing the promise of generative AI while drawing from critical lessons from past EdTech initiatives, particularly during the COVID-19 pandemic.
As an education specialist and co-lead of the World Bank’s EdTech Team, I have spent the past few years pioneering responsible AI-powered education programs in Nigeria, Peru, Brazil, and beyond. My experience has shown that the key to success is not the technology itself, but a holistic approach to program design: one that starts with the educational problem, considers the local context, and always puts people first.
In this blog, I share five practical insights to support policymakers and education leaders towards more responsible and effective integration pathways of generative AI in education. These lessons offer a framework to shape the so-called “AI revolution” to better address the global learning crisis.
1. Start with the educational problem, not the AI tool
Before considering AI tools as a potential solution, we must clearly define the educational challenge we aim to address. Are we seeking to improve teaching quality, provide targeted support to students falling behind in math, or enhance digital literacy? Simply incorporating AI does not guarantee usage or impact.
This aligns with the World Bank’s “Ask Why” principle: EdTech policies and projects must be grounded in a clear vision for educational change. Technology should be a means to an end, not the end itself. This ensures we do not adopt technology for its own sake, but as a targeted tool to solve a specific problem.
2. A focus on the purpose of the program: develop a strong theory of change
Effective program design should start with a clear theory of change, which is a description of how an intervention is supposed to deliver the desired changes, outlining the causal pathways and assumptions involved. In other words, it clarifies the link between activities, outputs, outcomes, and the ultimate goals. For example, teacher training (activity) may lead to new instructional practices (output), which in turn improve student engagement (outcome) and lead to learning gains (impact).
Having a strong theory of change will not only support a clear vision of how the AI tool may support the intended purpose of the program but also highlight the key activities and outputs needed for that to happen (for instance, teacher training, guidelines on AI literacy, focus groups with students or parents, etc.) and establish indicators that measure use, progress, and engagement.
Moreover, a well-articulated theory of change provides the foundation for impact evaluation, helping to build the evidence base on what works and what does not when integrating AI in education.
3. Context is critical: design for the needs of the place
Successful integration of AI depends on understanding the local context. Factors like connectivity, electricity, and device availability should shape the choice of tools and content. For example, in areas with limited devices such as laptops or desktops or intermittent Wi-Fi, using mobile phones can be a more suitable option, as seen in an initiative in Ghana.
Context should also inform content and tool selection. For example, training materials for teachers and students in Nigeria and Peru were customized to their respective cultures and local realities, avoiding a one-size-fits-all approach and ensuring relevance and accessibility.
