Strategic adaptation -- the ability to adjust interaction behavior in response to changing constraints and leverage -- is a central goal of negotiation training and an emerging target for AI coaching systems. However, adaptation is difficult to evaluate because adaptation-relevant moments arise unpredictably in typical tasks. We study a reusable dyadic negotiation testbed that employs a controlled midstream change in one party's outside alternative as a repeatable perturbation to stress-test adaptation. In a six-round chat-based negotiation study (N=100), the perturbation reliably reorganized interaction dynamics: transitions between integrative (cooperative) and distributive (positional) behaviors declined, behavioral diversity narrowed, and interactions drifted toward more distributive tactics. Critically, this distributive drift predicted worse relational experience net of objective outcomes, and adaptation patterns were path dependent on prior behavior. These results establish a methodological bridge for evaluating and comparing AI coaching systems on strategic adaptation as a process and identify failure modes and design targets for adaptive interaction support.

翻译：策略适应——即根据约束条件和议价能力的变化调整互动行为的能力——是谈判训练的核心目标，也是AI教练系统日益关注的研究方向。然而，适应能力难以评估，因为在典型任务中，与适应相关的关键时刻往往不可预测地出现。本研究开发了一个可复用的双人谈判测试平台，通过受控地在中途改变某一方的外部替代选项，形成可重复的扰动以对适应能力进行压力测试。在一项包含六轮基于聊天的谈判研究中（N=100），该扰动可靠地重组了互动动态：整合性（合作）行为与分配性（立场）行为之间的转换减少，行为多样性收窄，互动趋向于更多分配性策略。关键的是，这种分配性漂移在控制客观结果后仍预测了更差的关系体验，且适应模式对先前的行为存在路径依赖。这些结果为将策略适应作为过程来评估和比较AI教练系统建立了方法桥梁，同时为适应性互动支持系统识别了失效模式与设计目标。