Problem
Research questions and friction points this paper is trying to address.
scientific impact
AI integration
knowledge translation
career stage
institutional capability
Translating AI into scientific impact: Field context, career position, and institutional capability in AI-enabled research
🤖 AI Summary
This study investigates how the impact of artificial intelligence (AI) knowledge on scientific influence is moderated by disciplinary context, career stage, and institutional capacity. Drawing on the OpenAlex database, the authors construct AI-specific citation metrics and employ large-scale bibliometric analysis combined with hierarchical regression models to assess the effect of AI integration on paper citations five years post-publication. The research systematically uncovers “translational capacity” as the pivotal mechanism determining the scientific value of AI knowledge: while AI adoption generally enhances citation impact, benefits are highly heterogeneous. Senior scholars gain primarily through broad AI-related citations, whereas early-career researchers benefit more from deep integration of AI methods. Institutions with moderate AI capabilities realize the greatest gains, while elite institutions predominantly drive the dissemination of AI knowledge.
📝 Abstract
Artificial intelligence (AI) is increasingly embedded in scientific research, but its scientific value is unlikely to be distributed evenly. This study examines how AI knowledge integration is associated with scientific impact and asks who benefits from AI-related knowledge in science. Using large-scale bibliographic data, we measure AI integration through references to papers in the OpenAlex Artificial intelligence subfield and link it to five-year citation impact. The results show that AI references are generally associated with higher citation impact, but the returns vary substantially across scientific fields. Career stage also matters: senior scholars benefit more from the extensive margin of AI referencing, whereas junior scholars benefit more from intensive AI referencing and tend to cite newer and higher-impact AI papers. At the institutional level, returns are non-monotonic: institutions with intermediate AI capability achieve the largest proportional gains, while leading AI institutions are more deeply embedded in AI-centered knowledge spaces, attract more AI-related audiences, and more often become substitute citation gateways to cited AI sources. These findings suggest that the value of AI knowledge depends not only on technical capability, but also on translational capacity: the ability to make AI knowledge meaningful, legitimate, and useful across scientific communities.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI integration
scientific impact
translational capacity
career stage
institutional capability
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