Argumentative dialogues across political divides can reduce polarization, yet opportunities for citizens to engage with opposing views in accessible and structured ways remain limited. AI dialogue partners offer a scalable framework for such open-mindedness exercises, but how the format of human-AI dialogues shapes their benefits remains unclear. In a two-session online experiment, 469 US participants were assigned to argue either for or against their own attitude on a contested political issue with an AI chatbot. Our experimental findings show attitude-congruent dialogues produced greater immediate reduction in both affective and opinion polarization than attitude-incongruent dialogues. By contrast, attitude-incongruent dialogues elicited weaker cognitive state empathy than the non-AI reference task but increased cognitive trait empathy in the two-week period between sessions, suggesting the effects of active generation of attitude-incongruent arguments may emerge over time. These findings highlight dialogue design as a key determinant of effective AI-mediated behavioral interventions.

翻译：跨越政治分歧的辩论性对话可以减少极化，但公民以可及且结构化的方式接触对立观点的机会仍然有限。AI对话伙伴为这类开放思维练习提供了可扩展的框架，但人机对话的形式如何影响其益处尚不明确。在一项包含两次会议的网络实验中，469名美国参与者被分配与AI聊天机器人就一个有争议的政治议题支持或反对自己的立场。我们的实验结果表明，与态度不一致的对话相比，态度一致的对话在情感极化和观点极化上产生了更显著的即时降低效果。相比之下，态度不一致的对话所引发的认知状态共情弱于非AI参考任务，但在两次会议之间的两周内增加了认知特质共情，这表明主动生成态度不一致论据的效果可能随时间逐渐显现。这些发现强调了对话设计是有效AI介导行为干预的关键决定因素。