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

Guillermo Cruces, Diego Fernández Meijide, Sebastian Galiani, Ramiro H. Gálvez and María Lombardi ()

School of Government Working Papers from Universidad Torcuato Di Tella

Abstract: Does generative artificial intelligence (AI) reinforce or reduce productivity differences across workers? Existing evidence largely studies AI within firms and occupations, where organizationalselectioncompresseseducationalheterogeneity,leavingunclearwhetherAI narrows productivity gaps across individuals with substantially different levels of formal education. Weaddressthisquestionusingarandomizedonlineexperimentconductedoutside firms, in which1,174 adults aged 25–45 with heterogeneous educational backgrounds complete an incentivized, workplace-style business problem-solving task. The task is a general (not domain-specific) exercise, and participants perform it either with or without access to a generative-AI assistant. Unlike prior work that studies heterogeneity within relatively homogeneous worker samples, our designtargets the between–education-group productivity gap as the primary estimand. We find that AI increases productivity for all participants, with substantially larger gains for lower-education individuals. In the absence of AIaccess, higher-education participants outperform lower-education participants by0.548standarddeviations; withAIaccess, thisgapfallsto0.139standarddeviations, implying that generative AI closes three-quarters of the initial productivity gap. We interpret this pattern as evidence that generative AI narrows effective productivity differences in task execution by relaxing constraints that are more binding for lower-education individuals, even though underlying skill differences remain, as reflected in persistent education gaps in task performance and in a follow-up exercise without AI assistance.

Keywords: Productivity; artificial intelligence; education; human capital; inequality (search for similar items in EconPapers)
JEL-codes: J24 O33 (search for similar items in EconPapers)
Pages: 96 pages
Date: 2026-03
New Economics Papers: this item is included in nep-ain, nep-edu, nep-exp and nep-lma
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