通过选句插入回复，让用户灵活控制AI参与程度。
Content-Driven Local Response: Supporting Sentence-Level and Message-Level Mobile Email Replies With and Without AI
- 用户可选句子插入回复，引导AI生成更贴合内容的建议。
- 实验显示该方法减少打字量且降低错误率，用户满意度高。
- 适合需要快速高效回复邮件的职场人士使用。
移动端邮件场景多样，对效率要求高，促使人们使用AI辅助回复。但自动生成的内容往往不符合用户真实意图，导致用户在使用AI时面临权衡难题，而现有邮件界面尚未考虑这一问题。为此，本文提出一种名为内容驱动局部响应（Content-Driven Local Response, CDLR）的新界面设计，灵感来自微任务机制。用户可通过选择邮件中的特定句子来插入回复，同时此操作也用于引导AI生成建议。该设计支持局部建议与整消息层面改进的结合。基于126名用户的对照实验，结果表明：CDLR能够支持不同程度的AI参与，兼顾高效与可控性，显著降低输入负担与出错率。本研究贡献在于重新定义了人机协同工作流的界面设计范式，使用户能动态调节AI介入强度。
原文摘要 · Abstract (English)
Mobile emailing demands efficiency in diverse situations, which motivates the use of AI. However, generated text does not always reflect how people want to respond. This challenges users with AI involvement tradeoffs not yet considered in email UIs. We address this with a new UI concept called Content-Driven Local Response (CDLR), inspired by microtasking. This allows users to insert responses into the email by selecting sentences, which additionally serves to guide AI suggestions. The concept supports combining AI for local suggestions and message-level improvements. Our user study (N=126) compared CDLR with manual typing and full reply generation. We found that CDLR supports flexible workflows with varying degrees of AI involvement, while retaining the benefits of reduced typing and errors. This work contributes a new approach to integrating AI capabilities: By redesigning the UI for workflows with and without AI, we can empower users to dynamically adjust AI involvement.
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