AI谈判助手效果不如传统手册，揭示了递归式准备的深层逻辑
AI-Mediated Negotiation: Design Reflections and Lessons
- 基于四条认知假设构建对话式辅导系统Trucey
- 267人实验显示静态手册在赋能与易用性上优于AI方案
- 提出'先建图再规划再模拟'的设计新原则，适配复杂协商场景
对话式AI有望为高风险职场谈判提供个性化、互动式准备，能模拟真实阻力。我们构建了基于理论的辅导系统Trucey，包含四项假设：表达促进澄清、个性化提升策略能力、分块降低认知负荷、结构化支持减轻元认知负担。预注册实验（N=267）与访谈（N=15）挑战了这些假设。值得注意的是，作为被动对照的静态手册在赋能感和可用性上均优于两个AI条件。原因在于：每个假设对应一种线性准备模型，而谈判准备本质是递归过程。研究揭示现有HAI设计指南存在未明示的适用范围限制，并提出未来AI辅导设计应遵循‘先建图、再规划、后模拟’的顺序原则。
原文摘要 · Abstract (English)
Conversational AI promises a new kind of preparation for high-stakes workplace negotiations -- personalized, interactive, and capable of simulating realistic resistance. That promise is intuitive. We built Trucey, a theory-driven coaching system, to test it. The system encoded four assumptions: that articulation supports clarification, that personalization builds strategic competence, that chunked delivery reduces cognitive load, and that structured scaffolding removes metacognitive burden. A pre-registered experiment (N=267) and interviews (N=15) complicated each of them. Notably, the static handbook we included as a passive control outperformed both AI conditions on empowerment and usability. We reflect on why: each assumption encoded a specific model of how preparation unfolds, and the findings revealed that conversational AI imposes a linear execution model on a task that is fundamentally recursive. We identify an unexamined scope condition on established HAI design guidelines and close with a sequencing principle -- map before path, path before simulation -- for future AI coaching design.
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