AI-Mediated Negotiation: Design Reflections and Lessons
For designers of AI coaching systems, this work identifies a scope condition on HAI guidelines and proposes a sequencing principle, though the finding is incremental as it refines existing knowledge.
The authors built a theory-driven AI coaching system (Trucey) for negotiation preparation, but a pre-registered experiment (N=267) found that a static handbook outperformed both AI conditions on empowerment and usability, revealing that conversational AI imposes a linear execution model on a recursive task.
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.