Translating AI into scientific impact: Field context, career position, and institutional capability in AI-enabled research

• URL: http://arxiv.org/abs/2607.16780v1
• Date: Sat, 18 Jul 2026 11:31:04 GMT
• Title: Translating AI into scientific impact: Field context, career position, and institutional capability in AI-enabled research
• Abstract summary: This study examines how AI knowledge integration is associated with scientific impact and asks who benefits from AI-related knowledge in science.<n>Using large-scale data, we measure AI integration through references to papers in the OpenAlex Artificial intelligence subfield.<n>The results show that AI references are generally associated with higher citation impact, but the returns vary substantially across scientific fields.
• Score: 0.6050783704803797
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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