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From cheap answers to lasting learning: AI tutoring and educational inequality

Author

Listed:
  • Dang, Hai-Anh
  • Behrman, Jere

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.

Suggested Citation

  • Dang, Hai-Anh & Behrman, Jere, 2026. "From cheap answers to lasting learning: AI tutoring and educational inequality," GLO Discussion Paper Series 1861, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:1861
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    Keywords

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    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • H52 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Education
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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