Translating AI into scientific impact: Field context, career position, and institutional capability in AI-enabled research
Computers and SocietyDigital Libraries
Summary
The authors studied how using AI knowledge affects the impact of scientific papers. They found that papers referencing AI research generally get more citations, but this benefit differs by field and career stage. Senior researchers gain more by broadly including AI, while junior ones benefit more from deeply focusing on recent, high-quality AI work. Institutions with moderate AI expertise see the biggest citation boosts, unlike top AI institutions that are already deeply connected to AI knowledge. Overall, the authors say that success with AI depends not just on technical skill but also on how well researchers apply AI knowledge across different scientific areas.
Authors
Zhiyong Tan, Hongkan Chen, Yi Bu
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.