Date
Publisher
arXiv
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.
Study at a glance
- Question: Does generative artificial intelligence reduce or reinforce productivity gaps between individuals with different levels of formal education?
- Setting and data: Online experiment in Argentina, September to November 2025, with 1,174 adults aged 25 to 45.
- Method: Randomized online experiment with an immediate unassisted follow-up module.
Why it matters
Technological innovations historically widened labor market inequalities. Whether generative artificial intelligence weakens the link between formal education and productivity or instead reproduces existing skill advantages remains an open empirical question.
Key findings
- AI assistance closed about three-quarters of the baseline performance gap between higher- and lower-education participants, raising scores for both groups but with substantially larger gains for lower-education adults.
- Removing AI did not cause treated participants to perform worse than control participants, and lower-education adults retained part of their performance gain in an unassisted follow-up.
- Follow-up performance without AI was substantially higher only when participants combined intensive AI assistance with sustained engagement and time spent on the task.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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