Does Artificial Intelligence Advance Science?
2026-06-03 • Computers and Society
Computers and Society
AI summaryⓘ
The authors studied over a million scientific papers to see if using AI helps make science more creative. They found that papers involving AI are more likely to be among the most creative, but different ways of using AI lead to different types of creativity. Using AI tools on existing problems boosts creativity by combining ideas in new ways, while changing AI models for specific tasks leads to more new concepts. This shows AI helps science through multiple paths, not just one. Their work suggests we need new methods to evaluate creative science depending on how AI is used.
Artificial Intelligence (AI)Scientific CreativityNoveltyRecombinant NoveltyObject NoveltyCitation ImpactAI Research ModesTool-oriented AIAdaptation-oriented AIScience Policy
Authors
Liangping Ding, Cornelia Lawson, Philip Shapira
Abstract
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.