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

2026-08-04

AI Summary

This study investigates whether generative AI can reduce productivity disparities among workers with differing educational backgrounds. In a randomized online experiment involving 1,174 adults aged 25–45, participants completed workplace tasks both with and without AI assistance, followed by an unassisted performance assessment. The results demonstrate that generative AI significantly improves performance across all groups, with individuals from lower-education backgrounds benefiting disproportionately. Consequently, the education-based performance gap narrowed from 0.548 to 0.139 standard deviations—a reduction of approximately 75%. This work provides the first large-scale randomized controlled evidence that AI can effectively mitigate educational inequality in task performance and further reveals that intensive AI use, when coupled with sustained individual effort, enhances subsequent unassisted performance.

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.

Problem

• generative AI
• productivity gaps
• education
• human capital
• workplace performance

Innovation

• generative AI
• productivity gap
• randomized experiment
• human capital
• AI-assisted performance