AI-Augmented Peer Review and Scientific Productivity: A Cross-Country Panel and SEM Analysis

TL;DR

This paper empirically demonstrates that AI-augmented peer review systems significantly boost scientific productivity, reproducibility, and innovation across OECD countries, using a novel AI Review Capability Index and advanced statistical analysis.

Contribution

It introduces the AI Review Capability Index (AIRC) and provides the first cross-country empirical validation of AI's positive impact on scientific evaluation and productivity.

Findings

A one standard deviation increase in AIRC correlates with an 18-25% rise in scientific productivity.

AI-assisted review improves research reproducibility and reduces quality variance.

The study uses fixed-effects regression and SEM to establish causal links.

Abstract

This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little empirical work has quantified the systemic impact of AI integration at the national level. We construct a novel AI Review Capability Index (AIRC) and examine its effects on research productivity, reproducibility, and innovation output. Using fixed-effects regression and structural equation modeling (SEM), we show that AI-assisted evaluation significantly enhances productivity and reduces variance in research quality. Results indicate that a one standard deviation increase in AIRC is associated with an 18-25% increase in scientific productivity, mediated through improvements in review efficiency and reproducibility. This paper provides the first cross-country empirical validation of AI-augmented scientific evaluation systems and contributes to the emerging literature on AI as a structural driver of knowledge production.