发表机构
University of Minnesota; Allen AI Institute（明尼苏达大学; 艾伦人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过纵向研究与访谈，揭示跨学科研究人员如何编排生成式AI以填补知识空白，同时发现专业知识悖论，并提出校准验证、促进跨域综合及适应学科惯例的设计建议。
AI 中文摘要
随着研究人员应对跨学科问题，他们面临着在深化主要领域专业知识的同时快速获取次要领域知识的双重需求。生成式AI（GenAI）日益被定位为满足这一需求的关键工具，从通用聊天助手到被市场宣传为自主研究代理的深度研究工具。先前的研究考察了研究人员如何使用GenAI支持单一学科或一般性研究任务。然而，我们对跨学科研究中的目标及GenAI实践知之甚少。我们开展了一项纵向研究，并对15位跨学科研究人员进行了半结构化访谈，以考察他们实际上如何编排GenAI。研究结果显示，研究人员依赖GenAI来填补知识空白，同时保持对新颖性发现的认知能动性。我们还揭示了一个专业知识悖论：GenAI的输出在最需要时最难验证。我们的实证见解推动了GenAI设计的改进，使其能够根据研究人员的专业知识校准验证，促进跨领域综合，并使提示词和输出适应学科惯例。
英文摘要
As researchers tackle interdisciplinary problems, they face the need to deepen expertise in primary areas while rapidly acquiring knowledge in secondary domains. Generative AI (GenAI) is increasingly positioned to meet this need, from general-purpose chat assistants to Deep Research tools marketed as autonomous research agents. Prior work has examined how researchers use GenAI to support single-discipline or general research tasks. However, we know little about the goals and GenAI practices in interdisciplinary research. We conducted a longitudinal study and semi-structured interviews with 15 interdisciplinary researchers to examine how interdisciplinary researchers actually orchestrate GenAI. Findings show that researchers leaned on GenAI to fill knowledge gaps while maintaining epistemic agency for novelty discovery. We also uncovered an expertise paradox: GenAI outputs were hardest to verify when most needed. Our empirical insights motivate GenAI designs that calibrate verification to researchers' expertise, nudge toward cross-domain synthesis, and adapt prompting and outputs to disciplinary conventions.
