Generative AI shifts interaction toward intent-based outcome specification, despite user intents being inherently vague, fluid, and evolving. While a growing body of HCI research has proposed diverse interaction techniques to support this process, there is limited understanding of what the key aspects of intent communication are and how they interplay to shape users' workflows. To bridge this gap, we first conduct a systematic literature review of 46 HCI papers and identify four core aspects of intent communication support: intent articulation, exploration, management, and synchronization. To investigate how these aspects interplay in practice, we developed IntentFlow, a research probe that embodies all four aspects for a writing task, and conducted a comparative study (N=12). Our action-level behavioral analysis reveals that comprehensive support enables verification-driven refinement and progressive intent curation, reduces cognitive effort, and improves users' sense of control and understanding of intent-output alignment. We conclude with design implications for building generative AI systems that support intent communication as a dynamic, iterative process.

翻译：生成式人工智能将交互转向基于意图的结果指定，尽管用户意图本质上是模糊、流动且不断演变的。尽管越来越多的人机交互研究提出了多种交互技术来支持这一过程，但对于意图交流的关键方面是什么以及它们如何相互作用以塑造用户的工作流程，目前理解有限。为弥合这一差距，我们首先对46篇人机交互论文进行了系统性文献综述，识别出意图交流支持的四个核心方面：意图表达、探索、管理和同步。为探究这些方面在实践中如何相互作用，我们开发了IntentFlow——一个为写作任务体现所有四个方面的研究探针，并开展了一项比较研究（N=12）。我们的行动级行为分析表明，全面的支持能够实现验证驱动的细化和渐进式意图管理，降低认知负荷，并提升用户对意图-输出对齐的控制感和理解。最后，我们提出了设计启示，以构建支持意图交流作为动态迭代过程的生成式人工智能系统。