Working Paper

Artificial intelligence is rapidly reshaping how education evidence is found, screened, synthesised, translated, and communicated. For education systems in low- and middle-income countries (LMICs), this shift presents a significant opportunity: AI tools can dramatically reduce the time and cost required to navigate large and fragmented evidence bases, making research more accessible to policymakers, practitioners, and funders. At the same time, the risks are substantial. Poorly governed AI use can introduce confident inaccuracies, obscure methodological weaknesses, and reinforce existing global inequities in whose evidence is visible and valued.

This working paper provides a practical, principles-based guide to using AI responsibly across the education evidence ecosystem in LMIC contexts.

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