Large Language Model-based Voice Assistants (LLM-VAs) are increasingly deployed in assistive settings for older adults, yet little is known about how an agent's personality shapes user perceptions of its explanations. This paper presents a mixed factorial experiment (N=140) examining how agreeableness and extraversion in an LLM-VA ("Robin") influence older adults' perceptions across seven measures: empathy, likeability, trust, reliance, satisfaction, intention to adopt, and perceived intelligence. Results reveal that high agreeableness drove stronger empathy perceptions, while low agreeableness consistently penalized likeability. Importantly, perceived intelligence remained unaffected by personality, suggesting that personality shapes sociability without altering competence perceptions. Real-time environmental explanations outperformed conversational history explanations on five measures, with advantages concentrated in emergency contexts. Notably, highly agreeable participants were especially critical of low-agreeableness agents, revealing a user-agent personality congruence effect. These findings offer design implications for personality-aware, context-sensitive LLM-VAs in assistive settings.

翻译：基于大语言模型的语音助手（LLM-VA）正越来越多地被部署在面向老年人的辅助环境中，然而，智能体的个性如何影响用户对其解释的感知，目前尚知之甚少。本研究进行了一项混合因素实验（N=140），考察LLM-VA（"Robin"）中的宜人性和外向性如何影响老年人在七个维度上的感知：共情、喜爱度、信任、依赖度、满意度、采用意向以及感知智能。结果表明，高宜人性显著增强了共情感知，而低宜人性则持续降低了喜爱度。重要的是，感知智能不受个性的影响，这表明个性塑造了社交性，但并未改变能力感知。在五个维度上，实时环境解释优于对话历史解释，且其优势主要集中在紧急情境中。值得注意的是，高宜人性的参与者对低宜人性智能体尤为挑剔，揭示了用户与智能体之间的个性一致性效应。这些发现为辅助环境中具有个性感知和情境感知能力的LLM-VA提供了设计启示。