人机协作界面框架在智能用户界面生态系统中的应用
Interface Framework for Human-AI Collaboration within Intelligent User Interface Ecosystems
AI总结：
本文提出基于工作流程复杂性、AI自主性和推理的框架，用于设计可扩展的AI接口，平衡AI自主性与人类监督。
摘要：
随着接口从静态用户路径发展为动态的人工智能协作模式，目前尚无标准方法可以根据用户需求和任务复杂性选择合适的接口模式。现有框架仅提供指导原则用于设计人工智能代理能力。我们提出一个基于工作流程复杂性、人工智能自主性和人工智能推理的维度框架，以指导设计具有上下文感知和可扩展性的AI接口，即模态（例如提示栏、分屏、全屏等）。该框架通过与市场营销产品设计师共同设计研讨会开发，并通过与八名长期人工智能用户进行定性研究进行完善。该研究评估了三个维度，确定了任务与接口之间的关系，并揭示了在所有高自主性场景中业务影响和安全风险的重要性。该框架为产品团队提供了一种共享的语言来开发可扩展的AI接口，强调接口之间的流动性以及逐步的用户控制，以在人工智能自主性与人类监督之间取得平衡。
英文摘要：
As interfaces evolve from static user pathways to dynamic human-AI collaboration, no standard methods exist for selecting appropriate interface patterns based on user needs and task complexity. Existing frameworks only provide guiding principles for designing AI agent capabilities. We propose a dimensional framework based on workflow complexity, AI autonomy, and AI reasoning to guide the design of context-aware, scalable AI interfaces aka modalities (e.g., prompt bars, split screens, full screens, etc.). The framework was developed through co-design workshops with designers of marketing products and refined through qualitative research with eight long-term AI users. The study evaluated the three dimensions, identified task-to-interface relationships, and surfaced the importance of both business impact and security risk across all high-autonomy scenarios. This framework provides product teams with a shared language to develop scalable AI interfaces, emphasizing fluidity between interfaces and progressive user control to balance AI autonomy with human oversight.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
