AI 中文总结
研究探讨人工智能知识整合与科学影响力的关系及谁受益，利用文献数据衡量人工智能整合与引文影响，发现领域、职业阶段、机构层面回报有差异，表明人工智能知识价值取决于技术与转化能力。
AI 中文摘要
人工智能日益融入科学研究，但其科学价值分布不均。本研究探讨人工智能知识整合与科学影响力的关联以及科学领域中谁从人工智能相关知识中受益。利用大规模文献数据，通过引用OpenAlex人工智能子领域的论文衡量人工智能整合，并将其与五年引文影响力联系起来。结果表明，人工智能引用通常与更高的引文影响力相关，但各科学领域回报差异很大。职业阶段也很重要：资深学者从人工智能引用的广度中受益更多，而初级学者从密集的人工智能引用中受益更多，且倾向于引用更新和影响力更高的人工智能论文。在机构层面，回报是非单调的：具有中等人工智能能力的机构获得的比例收益最大，而领先的人工智能机构更深入地嵌入以人工智能为中心的知识空间，吸引更多人工智能相关受众，且更常成为被引用人工智能来源的替代引用渠道。这些发现表明，人工智能知识的价值不仅取决于技术能力，还取决于转化能力，即让人工智能知识在科学界变得有意义、合理且有用的能力。
英文摘要
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.
