The Shift Towards Preprints in AI Policy Research: A Comparative Study of Preprint Trends in the U.S., Europe, and South Korea

TL;DR

This study analyzes how disruptive events like COVID-19 and ChatGPT influenced the adoption of preprints in AI policy research across the U.S., Europe, and South Korea, revealing regional differences in growth patterns from 2015 to 2024.

Contribution

It provides a comparative bibliometric analysis of preprint adoption in AI policy research across regions, highlighting the impact of global disruptions and regional research cultures.

Findings

US showed sharp, event-driven increases in preprint citations.

Europe experienced institutional growth in preprint adoption.

South Korea maintained steady, linear growth in preprints.

Abstract

The adoption of open science has quickly changed how artificial intelligence (AI) policy research is distributed globally. This study examines the regional trends in the citation of preprints, specifically focusing on the impact of two major disruptive events: the COVID-19 pandemic and the release of ChatGPT, on research dissemination patterns in the United States, Europe, and South Korea from 2015 to 2024. Using bibliometrics data from the Web of Science, this study tracks how global disruptive events influenced the adoption of preprints in AI policy research and how such shifts vary by region. By marking the timing of these disruptive events, the analysis reveals that while all regions experienced growth in preprint citations, the magnitude and trajectory of change varied significantly. The United States exhibited sharp, event-driven increases; Europe demonstrated institutional growth; and South Korea maintained consistent, linear growth in preprint adoption. These findings suggest that global disruptions may have accelerated preprint adoption, but the extent and trajectory are shaped by local research cultures, policy environments, and levels of open science maturity. This paper emphasizes the need for future AI governance strategies to consider regional variability in research dissemination and highlights opportunities for further longitudinal and comparative research to deepen our understanding of open-access adoption in AI policy development.