提出AI协作三维度框架，帮设计更安全好用的人机协同体验。
Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI
- 从显著性、参与度、活动性三维度定义人机协作机制。
- 提炼四类协作区与输入分类，支持设计可落地的交互模式。
- 适合产品设计师、AI开发者及跨团队沟通使用。
随着生成式和自主型AI融入日常产品，设计者面临核心挑战：如何构建人机协同——即用户与AI系统通过界面持续相互调整，以保障可用性、信任与安全。现有资源仅提供高层原则（如“保持透明”）或底层界面模式，缺乏中层设计知识。通过对60个商业AI应用的景观与产物分析，本文提出一个框架，将人机协同定义为三个维度的互动：显著性（AI呈现的突出程度）、参与度（用户可与AI互动的方式）和活动性（AI实际执行的任务）。我们贡献了中层工具，包括四类协作区（代劳型、监督型、协作型、无干预型）、输入类型分类（提示、激发、推断、叠加）、协调曲线用于映射用户旅程，并展示框架的生成能力。该框架可生成式地支持体验设计，分析性地评估现有系统，也可作为跨团队沟通的表达工具。
原文摘要 · Abstract (English)
As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination -- the ongoing mutual adjustment between users and AI systems as mediate through interfaces-that supports usability, trust, and safety. Existing resources offer high-level principles ("be transparent," "maintain user control") or low-level UI patterns, but there is a lack of mid-level design knowledge bridging the two. Through landscape and artifact analysis of 60 commercial AI applications, we introduce a framework defining human-AI coordination as the interplay of three dimensions: salience (how prominently AI is presented), involvement (what users can do to engage AI), and activity (what AI actually does). We contribute mid-level tools including coordination zones (done-for-me, done-under-me, done-with-me, done-without-me), an input taxonomy (prompted, sparked, inferred, layered), coordination curves for mapping user journeys, and design patterns demonstrating the generative capacity of the framework. The framework can be applied generatively to design experiences, analytically to evaluate existing ones, and communicatively to articulate ideas across stakeholders.
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