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.

翻译：本研究利用OECD国家的面板数据，实证考察了AI辅助同行评审系统对科研生产率的影响。尽管已有研究揭示了传统同行评审的低效问题，但鲜有实证工作从国家层面量化AI整合的系统性影响。我们构建了全新的AI评审能力指数(AIRC)，并检验了其对研究生产率、可复现性和创新产出的影响。通过固定效应回归和结构方程模型(SEM)，研究表明AI辅助评审显著提升了生产率，并降低了研究质量的变异。结果显示，AIRC每提高一个标准差，科研生产率将增加18-25%，这一效应通过评审效率和可复现性的改善实现中介传导。本文首次提供了AI辅助科研评审系统的跨国实证验证，并为AI作为知识生产结构性驱动力的新兴文献做出了贡献。