Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment

Guillermo Cruces, Diego Fernandez Meijide, Sebastian Galiani, Ramiro Galvez and Maria Lombardi
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Guillermo Cruces: University of Nottingham
Diego Fernandez Meijide: Universidad de San Andres
Sebastian Galiani: Tulane University
Ramiro Galvez: UTDT
Maria Lombardi: UTDT

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Abstract: Does generative artificial intelligence (AI) widen or narrow productivity gaps across workers? We study this in a randomized online experiment with 1,174 adults aged 25-45 who completed a workplace-style problem-solving task with or without a generative AI assistant, followed by an unassisted module. AI improves performance for all participants, but gains are larger among those with less education. Without AI, higher-education participants outperform lower-education participants by 0.548 standard deviations; with AI, the gap falls to 0.139, closing about three-quarters of the initial difference. Chat logs show that lower-education participants obtain substantial assistance, while higher-education participants use AI more effectively. Gains are not purely due to delegation: treated participants do not perform worse once AI is removed, and lower-education participants retain part of their improvement, although a sizable gap re-emerges. Intensive AI use raises assisted performance regardless of participants' own effort, but follow-up performance improves only when intensive use is combined with sustained effort. Generative AI narrows effective productivity differences in task execution, while human-capital differences continue to shape unassisted performance and tool use.

Date: 2026-08
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