Generative AI and the Productivity Divide: Human-AI Complementarities in Education

TL;DR

This study shows that generative AI boosts productivity in education but benefits users unevenly, depending on their AI interaction skills, and suggests training to reduce inequality.

Contribution

It provides experimental evidence on how AI interaction competence influences productivity gains and proposes interventions to promote equitable benefits.

Findings

GenAI access increases average task performance.

High-AIC users gain more from GenAI, while low-AIC users may see limited or negative gains.

Standardized workflows can reduce outcome variability.

Abstract

Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled experiment in which participants-analogs of early-career knowledge workers-were assigned to self-study a technical domain using either traditional resources or large-language-model (LLM) assistance. On average, GenAI access significantly increased task performance, but the distribution of gains was highly uneven. Improvements were not predicted by GPA or prior knowledge, but by \textit{AI Interaction Competence (AIC)} -- the ability to elicit, filter, and verify model outputs. High-AIC participants realized outsized gains; low-AIC participants saw limited or even negative marginal returns. A scaffolding intervention (conceptual maps) reduced outcome variance, indicating that standardized workflows can mitigate inequality in AI-mediated performance. We interpret these findings through the lens of human-AI complementarities: GenAI raises mean productivity while introducing a new axis of capability inequality. Managerially, firms should pair GenAI access with short AIC micro-training and simple standard operating procedures to capture value consistently and avoid uneven adoption outcomes.