From Cheap Answers to Lasting Learning: AI Tutoring and Educational Inequality

Hai-Anh Dang () and Jere Behrman ()
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Hai-Anh Dang: World Bank
Jere Behrman: University of Pennsylvania

No 19072, IZA Discussion Papers from IZA Network @ LISER

Abstract: Nearly half of 15-year-old students in OECD countries, and more in some poorer countries, use AI weekly for learning. Generative AI makes schoolwork help cheap for connected students, but cheaper help need not yield lasting learning. We link the economics of supplementary education with learning science to examine how effort, entry costs, public provision and assessment shape AI tutoring’s benefits. Randomized trials find gains and losses varying by setting, use and outcome, while lasting, equitable gains remain uncertain. We distinguish program effects from generative AI’s contribution, compare full costs with alternatives, and propose decision rules and a trial design.

Keywords: artificial intelligence; tutoring; learning; educational inequality; public investment; randomized trials (search for similar items in EconPapers)
JEL-codes: H52 I21 I24 I28 O33 (search for similar items in EconPapers)
Date: 2026-11
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