As Generative AI (GenAI) capabilities expand, understanding how to preserve and develop human expertise while leveraging AI's benefits becomes increasingly critical. Through empirical studies in two contexts -- survey article authoring in scholarly research and business document sensemaking -- we examine how domain expertise shapes patterns of AI delegation and information processing among knowledge workers. Our findings reveal that while experts welcome AI assistance with repetitive information foraging tasks, they prefer to retain control over complex synthesis and interpretation activities that require nuanced domain understanding. We identify implications for designing GenAI systems that support expert cognition. These include enabling selective delegation aligned with expertise levels, preserving expert agency over critical analytical tasks, considering varying levels of domain expertise in system design, and supporting verification mechanisms that help users calibrate their reliance while deepening expertise. We discuss the inherent tension between reducing cognitive load through automation and maintaining the deliberate practice necessary for expertise development. Lastly, we suggest approaches for designing systems that provide metacognitive support, moving beyond simple task automation toward actively supporting expertise development. This work contributes to our understanding of how to design AI systems that augment rather than diminish human expertise in document-centric workflows.

翻译：随着生成式人工智能能力的扩展，如何在利用AI优势的同时保持和发展人类专业知识变得日益关键。通过在学术研究中的综述文章撰写与商业文档意义建构这两个情境下的实证研究，我们探讨了领域专业知识如何影响知识工作者对AI委托模式和信息处理方式的选择。研究发现，虽然专家欢迎AI协助处理重复性信息检索任务，但他们更倾向于保留对需要细致领域理解的复杂综合与解释活动的控制权。我们提出了支持专家认知的生成式AI系统设计启示：包括实现与专业水平相匹配的选择性委托机制、保持专家对关键分析任务的主导权、在系统设计中考虑不同层级的领域专业知识、以及支持帮助用户校准依赖程度同时深化专业技能的验证机制。我们探讨了通过自动化减轻认知负荷与保持专业技能发展所需刻意练习之间的内在张力。最后，我们提出了提供元认知支持的系统设计方法，超越简单的任务自动化，转向积极支持专业知识发展。本研究为如何设计能够增强而非削弱人类专业知识的文档中心型工作流AI系统提供了新的理解。