The Digital Divide in Generative AI: Evidence from Large Language Model Use in College Admissions Essays

TL;DR

This study investigates how the adoption of large language models in college admissions essays varies by socioeconomic status and how it impacts admissions outcomes, revealing potential equity concerns and linguistic changes over time.

Contribution

It provides the first large-scale longitudinal analysis of LLM use in college admissions, linking socioeconomic disparities, linguistic shifts, and evaluative consequences.

Findings

LLM use increased sharply in 2024, especially among lower SES applicants.

Surface-level linguistic features converged post-2023, notably among fee-waived and rejected applicants.

Increased LLM use was associated with decreased predicted admission probabilities for lower SES applicants.

Abstract

Large language models (LLMs) have become popular writing tools among students and may expand access to high-quality feedback for students with less access to traditional writing support. At the same time, LLMs may standardize student voice or invite overreliance. This study examines how adoption of LLM-assisted writing varies across socioeconomic groups and how it relates to outcomes in a high-stakes context: U.S. college admissions. We analyze a de-identified longitudinal dataset of applications to a selective university from 2020 to 2024 (N = 81,663). Estimating LLM use using a distribution-based detector trained on synthetic and historical essays, we tracked how student writing changed as LLM use proliferated, how adoption differed by socioeconomic status (SES), and whether potential benefits translated equitably into admissions outcomes. Using fee-waiver status as a proxy for SES, we observe post-2023 convergence in surface-level linguistic features, with the largest changes in fee-waived and rejected applicants. Estimated LLM use rose sharply in 2024 across all groups, with disproportionately larger increases among lower SES applicants, consistent with an access hypothesis in which LLMs substitute for scarce writing support. However, increased estimated LLM use was more strongly associated with declines in predicted admission probability for lower SES applicants than for higher SES applicants, even after controlling for academic credentials and stylometric features. These findings raise concerns about equity and the validity of essay-based evaluation in an era of AI-assisted writing and provide the first large-scale longitudinal evidence linking LLM adoption, linguistic change, and evaluative outcomes in college admissions.