This paper examines whether and how artificial intelligence (AI) advances scientific creativity. Drawing on scientific publications, the primary output of researchers, we analyze over one million publications from OpenAlex to investigate the relationship between AI adoption and multiple dimensions of scientific creativity, including novelty (recombinant novelty and object novelty) and impact (3-year short-run citation impact and 10-year long-run citation impact). We find that AI publications are significantly more likely to achieve top-decile creativity relative to non-AI publications, with 5.5 to 10.2 percentage point higher likelihood to rank in the top creativity decile. Critically, we uncover substantial heterogeneity across AI research modes. Tool-oriented AI research, which applies existing AI models to domain tasks, is associated with the largest gains in recombinant-based creativity, while Adaptation-oriented AI research, modifying AI models for domain-specific problems, is associated with relatively higher object-based creativity. These findings reveal that AI does not advance science through a single mechanism but through structurally distinct creative pathways that depend on how AI is incorporated into the research process. Our results contribute to ongoing debates about AI's role in science and carry direct implications for research evaluation and science policy, highlighting the need for assessment frameworks that can distinguish between recombinant and conceptual forms of creativity and that recognize how different modes of AI adoption produce fundamentally different types of scientific contribution.

翻译：本文探讨了人工智能（AI）是否以及如何促进科学创造力。以科学出版物——研究人员的主要产出——为基础，我们分析了来自OpenAlex的超过一百万份出版物，以研究AI采纳行为与科学创造力多个维度之间的关系，这些维度包括新颖性（重组新颖性与对象新颖性）和影响力（3年短期引用影响与10年长期引用影响）。我们发现，与非AI出版物相比，AI出版物达到创造力前十名的可能性显著更高，其排名位于创造力前十名中的概率高出5.5至10.2个百分点。关键的是，我们揭示了不同AI研究模式之间存在显著的异质性。以工具为导向的AI研究（即将现有AI模型应用于领域任务）与基于重组的创造力提升最大相关，而以适应性为导向的AI研究（即针对特定领域问题修改AI模型）则与相对更高的基于对象的创造力相关。这些发现表明，AI并非通过单一机制推动科学进步，而是通过结构上不同的创造性路径——这些路径取决于AI如何被整合到研究过程中。我们的研究结果丰富了关于AI在科学中作用的现有辩论，并对研究评估和科学政策具有直接影响，强调了评估框架需要能够区分重组型与概念型的创造力形式，并认识到不同的AI采纳模式如何产生根本不同类型的科学贡献。