个性化以说服：情境化和温暖对对话式AI中信任与依赖的影响
Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
AI总结：
通过2x2被试间实验（N=380），研究情境化与对话温暖如何交互影响AI助手在反驳专家建议时的说服力与用户依赖，发现情境化降低说服力但与温暖结合通过交叉交互恢复，且AI素养解耦信任与行为。
摘要：
人工智能（AI）代理通过根据用户的背景、兴趣和先前交互来定制解释，即情境化，从而个性化其响应。个性化已被视为政治或营销中的说服策略。然而，在用户通常缺乏先验知识的日常任务中，情境化的说服效果仍不明确。我们进行了一项2×2被试间实验（N=380），研究情境化与对话温暖相结合如何影响AI助手在反驳专家建议时的依赖性和说服力。我们的发现表明，情境化降低了AI的说服力，但其与温暖的结合通过交叉交互恢复了说服力。对AI的依赖在所有条件下都存在，并且不受对话设计的影响。信任强烈预测说服力和依赖，但情境化和温暖都不通过信任起作用。AI素养解耦了信任与行为：素养更高的用户对助手报告的信任较低，但更易被说服且更依赖其建议。这些结果表明，用户倾向于依赖AI代理而非人类专家判断；然而，界面级别的对话设计选择在塑造行为方面的作用有限。
英文摘要：
Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, referred to as contextualization. Personalization has been identified as a persuasive strategy in politics or in marketing. However, the persuasive effect of contextualization in everyday tasks, where users often lack prior knowledge, remains unclear. We conducted a $2\times2$ between-subjects experiment ($N = 380$) examining how contextualization, combined with conversational warmth, shapes reliance and persuasiveness of an AI assistant arguing against expert recommendations. Our findings reveal that contextualization reduces the persuasive power of AI, but its combination with warmth restores persuasiveness through a crossover interaction. Reliance on AI is present across conditions and is invariant to the conversational design. Trust strongly predicts both persuasion and reliance, yet neither contextualization nor warmth operates through trust. AI literacy decouples trust from behavior: more literate users report lower trust in the assistant, yet are more persuaded and more reliant on its advice. These results suggest that users are prone to deferring to AI agents over human expert judgment; however, interface-level conversational design choices have a limited role in shaping the behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
