Why generative AI is widening the gender gap in academic research

By Dr. Liji Thomas, MDReviewed by Lily Ramsey, LLMFeb 12 2025

A new study finds that male researchers are leveraging AI tools more effectively, gaining a productivity advantage over their female counterparts. Can targeted interventions close the divide?

Study: Gender disparities in the impact of generative artificial intelligence: Evidence from academia. Image Credit: Owlie Productions/Shutterstock.com

Generative artificial intelligence (AI) is driving productivity gains across multiple fields, including academia. However, its impact appears to be uneven, benefiting male researchers more than their female counterparts. A recent study published in PNAS Nexus highlights this growing disparity.

Introduction

Generative AI is increasingly integrated into research workflows, assisting scientists with data collection, literature reviews, and analysis. By automating routine tasks, AI allows researchers to focus on innovative studies. In some cases, AI has enabled the rapid production of research papers within an hour, improving both speed and quality.

Given these advantages, generative AI is becoming a standard tool in academic research. In fact, 80% of Nature readers report having used ChatGPT or similar tools at least once. However, its adoption varies significantly, influenced by sociodemographic factors, job satisfaction, and workplace culture.

This discrepancy means that while some researchers experience substantial productivity gains, others lag behind, exacerbating existing inequalities in academia.

Both anecdotal evidence and survey data suggest that men are more likely than women to embrace generative AI. As a result, male researchers may produce more publications, accelerating their career progression while leaving their female counterparts at a disadvantage.

About the study

The study examined how ChatGPT influences research productivity across genders through two separate analyses.

Study 1: Analyzing research output

The first analysis focused on preprints uploaded to the Social Science Research Network (SSRN) between May 2022 and June 2023. SSRN, a major open-access repository, provided a rich dataset for assessing productivity trends. Researchers applied a difference-in-differences (DiD) approach to measure gender disparities in research output.

Initially, there was no observable change in productivity, likely due to the time required for researchers to familiarize themselves with AI tools. However, as AI adoption increased, male researchers exhibited a 6.4% relative boost in productivity compared to their female counterparts. Specifically, men were 0.0004 more likely than women to upload at least one preprint per month.

This gender gap widened by 57%, increasing from a 0.007 to a 0.011 probability difference in research output. To ensure a surge in AI-related papers didn’t skew these findings, researchers excluded publications explicitly discussing ChatGPT. The gap persisted, confirming that AI adoption was indeed driving the disparity.

Further analysis, accounting for co-authorship and individual contributions, reinforced these results. Notably, the quality of research—measured by abstract views—remained consistent, indicating that AI use boosted output without compromising rigor.

The productivity gap was most pronounced in countries where ChatGPT is widely available and used, such as the U.S., Australia, and Spain. This correlation underscores AI’s role in amplifying existing gender disparities.

Study 2: Attitudes toward AI

The second part of the study examined researchers’ attitudes and usage patterns regarding generative AI. Findings revealed that men used AI tools more frequently and for longer durations than women.

Male researchers also reported greater efficiency gains and were more likely to recommend AI tools to colleagues.

Importantly, these differences in productivity were linked to usage patterns rather than inherent gender traits. The more researchers engaged with AI, the greater the efficiency benefits they experienced.

Conclusions

Both male and female researchers have access to generative AI, yet men are leveraging it more effectively to increase their research output. This discrepancy appears to stem from differences in attitudes and behaviors toward technology adoption.

The introduction of generative AI may compound existing inequalities related to funding, leadership roles, access to research facilities, and evaluation metrics.

To prevent this technology from further widening the gender gap, it is crucial to actively encourage and train all researchers—particularly women—to integrate AI into their workflows.

Without proactive measures, female researchers risk falling behind in an increasingly AI-driven academic landscape.