AI 中文总结
本研究提出多角色AI工具StanceLab，通过对比三角色模式与独立LLM模式的试点，明确了设计需求并规划了未来工作流，助力用户准备讨论立场。
AI 中文摘要
对社会或社区问题的初始反应在成为正式表达前可能具有意义，但人们仍需明确主张、预判受众风险，并决定向他人展示多少推理过程。我们提出StanceLab，一款用于在讨论前准备立场的原型工具。该原型对比了两种模式：一是三角色模式，其中访谈者、导师和反对者会并行回应，帮助用户诊断和修正立场；二是独立大语言模型（LLM）模式。在包含6名参与者、12次任务会话的形成性被试内试点研究中，所有会话都在记事本中生成了简短的最终消息。该试点揭示了两项设计需求：角色应能诊断有用的盲点或反对意见，且并行回应需要协调支持。我们提出了一种未来的诊断与写作工作流，将基于角色的反思转化为有受众意识的选择性最终消息。
英文摘要
An initial reaction to a social or community issue can feel meaningful before it is ready to become a message: people still need to clarify the claim, anticipate audience risks, and decide how much reasoning should become visible to others. We present StanceLab, a prototype for preparing a stance before entering a discussion. The prototype compares a three-persona mode, where an Interviewer, Mentor, and Opponent respond in parallel to help users diagnose and revise a stance, with a standalone LLM mode. In a formative within-subject pilot with six participants and 12 task sessions, every session produced a short final message in the notepad. The pilot revealed two design requirements: persona roles should diagnose useful blind spots or objections, and parallel responses need coordination support. We propose a future diagnosis-and-writing workflow that turns persona-based reflection into selective, audience-aware final messages.
Comments5 pages, 3 figures, 2 tables. Accepted to TAICHI 2026 (https://taichi2026.taiwanchi.org/program)
