发表机构
AI Institute, Harvard Business School; Frontline Associates; Emory University School of Law; Laboratory for Innovation Science at Harvard; Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛商学院人工智能研究所; 前线协会; 埃默里大学法学院; 哈佛大学创新科学实验室; 哈佛约翰·A·保尔森工程与应用科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过随机实验发现，围绕专业任务结构设计的支架式AI工作流（尤其用户主导式）比聊天界面更能提升分析覆盖率、激发多样请求并降低主观努力，强调AI应结构化人机协作过程。
AI 中文摘要
通用人工智能让用户自行选择请求何种支持，但将专业任务所需支持的架构留给用户自行处理。我们考察了交互式工作流如何在不规定用户与AI互动方式的前提下嵌入专业任务结构。我们围绕同一谈判脚手架设计了两类支架式界面：一类呈现完整的AI分析，另一类支持用户主导的增量式开发。一项包含800名参与者的四条件随机实验将这些界面与无AI及AI聊天界面进行了比较。AI支持条件相比无辅助工作提升了准备覆盖率；支架式工作流相比聊天界面进一步提升了覆盖率。尽管支架式工作流产生的覆盖率相近，但用户主导的工作流引发了更广泛的分析请求类型，且主观努力更低。因此，专业脚手架不仅取决于所展示的结构，还取决于工作流如何组织用户与其互动。有效的专业AI必须构建用户与AI共同进行分析的方式。
英文摘要
General-purpose AI lets users choose what support to request, but leaves them to structure the support a professional task requires. We examine how interactive workflows can embed professional task structure without prescribing how users engage with AI. We designed two scaffolded interfaces around the same negotiation scaffold: one presented a completed AI analysis, while the other supported user-directed, incremental development. A four-condition randomized experiment with 800 participants compared these interfaces with no-AI and an AI chat interface. AI-supported conditions improved preparation coverage over unaided work; the scaffolded workflows further improved coverage over chat. Although the scaffolded workflows produced similar coverage, the user-directed workflow elicited a broader repertoire of analytic requests and lower subjective effort. Professional scaffolding therefore depends not only on displayed structure but on how workflows organize users' engagement with it. Effective professional AI must structure how users and AI build analysis together.
