Making AI Tutoring Work for Poorer Schools

Hai-Anh Dang ()

No 19025, IZA Discussion Papers from IZA Network @ LISER

Abstract: Generative AI can make schoolwork look better without making students better at it. AI tutors could reach families who cannot afford human tutors, but whether they narrow learning gaps depends on policy. Randomized trials in Ghana, Nigeria and Sierra Leone show that AI-supported programs can raise learning measured without AI. Yet they do not isolate what works, and a large trial in rural China found no gain from AI personalization over non-personalized practice. This essay suggests that education systems, in Africa and beyond, judge complete programs rather than chatbots: by what students learn without AI, who benefits, and how they compare with feasible alternatives at full cost.

Keywords: AI tutoring; generative AI; learning; inequality; poorer countries; Africa (search for similar items in EconPapers)
JEL-codes: I21 I24 I25 O15 O33 (search for similar items in EconPapers)
Date: 2026-10
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Working Paper: Making AI tutoring work for poorer schools (2026)
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