AI-Augmented Peer Review and Scientific Productivity: A Cross-Country Panel and SEM Analysis
It addresses the problem of low scientific productivity and reproducibility for researchers and policymakers by providing the first cross-country empirical validation of AI-augmented peer review, though it is incremental as it builds on prior qualitative insights.
This study tackled the problem of inefficiencies in traditional peer review by empirically investigating the impact of AI-augmented systems on scientific productivity, finding that a one standard deviation increase in AI Review Capability Index is associated with an 18-25% increase in productivity.
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.