AI-driven conversational coaching is increasingly used to support workplace negotiation, yet prior work assumes uniform effectiveness across users. We challenge this assumption by examining how individual differences, particularly personality traits, moderate coaching outcomes. We conducted a between-subjects experiment (N=267) comparing theory-driven AI (Trucey), general-purpose AI (Control-AI), and a traditional negotiation handbook (Control-NoAI). Participants were clustered into three profiles -- resilient, overcontrolled, and undercontrolled -- based on the Big-Five personality traits and ARC typology. Resilient workers achieved broad psychological gains primarily from the handbook, overcontrolled workers showed outcome-specific improvements with theory-driven AI, and undercontrolled workers exhibited minimal effects despite engaging with the frameworks. These patterns suggest personality as a predictor of readiness beyond stage-based tailoring: vulnerable users benefit from targeted rather than comprehensive interventions. The study advances understanding of personality-determined intervention prerequisites and highlights design implications for adaptive AI coaching systems that align support intensity with individual readiness, rather than assuming universal effectiveness.

翻译：以人工智能驱动的对话式辅导越来越多地被用于支持工作场所谈判，但以往的研究假设其效果对所有用户一致。我们通过研究个体差异，特别是人格特质如何调节辅导效果，来挑战这一假设。我们进行了一项受试者间实验（N=267），比较了理论驱动的人工智能（Trucey）、通用人工智能（Control-AI）和传统谈判手册（Control-NoAI）。参与者基于大五人格特质和ARC类型学被聚类为三种类型：坚韧型、过度控制型和欠控制型。坚韧型工作者主要通过手册获得了广泛的心理增益；过度控制型工作者在理论驱动的人工智能下表现出特定结果的改善；而欠控制型工作者尽管与框架互动，却表现出最小效果。这些模式表明，人格特质是超越阶段性定制的准备度预测因素：脆弱用户受益于针对性而非全面的干预。本研究推进了对人格决定干预先决条件的理解，并强调了自适应人工智能辅导系统的设计启示，即系统应根据个体准备度调整支持强度，而非假设普遍有效性。