AI-Mediated Negotiation: Design Reflections and Lessons

2026-06-20

AI Summary

This study investigates how conversational AI can effectively support preparation for high-stakes workplace negotiations while mitigating its potential interference with users’ cognitive processes and strategic reasoning. Through the development of Trucey—a theory-driven AI coaching system—and a preregistered controlled experiment complemented by user interviews and cognitive load assessments, the research uncovers a fundamental mismatch between the linear interaction patterns of conversational AI and the inherently recursive nature of negotiation tasks. The work proposes a phased coaching design principle—“map first, then path, then simulate”—and identifies overlooked boundary conditions in human-AI collaboration. Empirical findings reveal that a static manual outperformed AI-based support in perceived empowerment and usability, with none of the four core design hypotheses supported, thereby highlighting the current limitations of conversational AI in handling recursively complex tasks.

Abstract

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.

Problem

• AI-mediated negotiation
• conversational AI
• coaching system
• design assumptions
• recursive task

Innovation

• conversational AI
• AI-mediated negotiation
• human-AI interaction
• design principles
• recursive task modeling