Generative AI and the Productivity Divide: Human-AI Complementarities in Education
For managers and educators deploying generative AI, this paper identifies a new source of productivity inequality—AI Interaction Competence—and shows that simple interventions can mitigate it.
A randomized controlled experiment with early-career knowledge workers found that LLM assistance increased task performance on average, but gains were highly uneven, predicted by AI Interaction Competence (AIC) rather than GPA or prior knowledge. High-AIC participants saw large gains, while low-AIC participants saw limited or negative returns; a scaffolding intervention reduced outcome variance.
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.