性格作为关系基础设施：用户对融入人格特质的LLM消息的感知
Personality as Relational Infrastructure: User Perceptions of Personality-Trait-Infused LLM Messaging
AI总结：
研究探讨了基于人格特质的LLM消息对用户感知的影响，发现人格信息通过整体暴露而非单条信息优化提升个性化和适当性评价。
摘要：
数字行为改变系统越来越多地依赖重复的系统发起信息来支持用户在日常情境中的行为改变。LLM使这些信息能够在交互中保持一致的个性化，但尚不清楚这种个性化是改善单条信息，还是通过暴露模式塑造用户的感知。我们在此背景下探讨了LLM生成的JITAIs（短、情境感知的信息），这些信息在被认为合适的时间点交付，用于行为改变，以身体活动为应用领域。在一项受控的回顾性研究中，90名参与者评估了使用四种LLM策略生成的信息：基线提示、少量示例提示、微调模型和检索增强生成，每种策略均与和无Big Five人格特质结合使用，以在多个场景中产生人格对齐的沟通。使用顺序多级模型与组内-组间分解，我们区分了试验层面的影响，即人格信息是否提高对单条信息的评价，与个体层面的暴露影响，即接收更高比例人格信息的参与者是否表现出系统性不同的整体感知。结果表明没有试验层面的关联，但接收更高比例BFPT信息的参与者认为信息更个性化、更合适，并报告了较少的负面情绪。我们使用沟通适应理论进行事后分析。这些结果表明，行为改变系统中基于人格的个性化可能主要通过总体暴露而非单条信息优化发挥作用，这对适应性系统的设计和评估在持续的人机交互中有启示。需要现场纵向研究来验证这些发现的真实性。
英文摘要：
Digital behaviour change systems increasingly rely on repeated, system-initiated messages to support users in everyday contexts. LLMs enable these messages to be personalised consistently across interactions, yet it remains unclear whether such personalisation improves individual messages or instead shapes users' perceptions through patterns of exposure. We explore this question in the context of LLM-generated JITAIs, which are short, context-aware messages delivered at moments deemed appropriate to support behaviour change, using physical activity as an application domain. In a controlled retrospective study, 90 participants evaluated messages generated using four LLM strategies: baseline prompting, few-shot prompting, fine-tuned models, and retrieval augmented generation, each implemented with and without Big Five Personality Traits to produce personality-aligned communication across multiple scenarios. Using ordinal multilevel models with within-between decomposition, we distinguish trial-level effects, whether personality information improves evaluations of individual messages, from person-level exposure effects, whether participants receiving higher proportions of personality-informed messages exhibit systematically different overall perceptions. Results showed no trial-level associations, but participants who received higher proportions of BFPT-informed messages rated the messages as more personalised, appropriate, and reported less negative affect. We use Communication Accommodation Theory for post-hoc analysis. These results suggest that personality-based personalisation in behaviour change systems may operate primarily through aggregate exposure rather than per-message optimisation, with implications for how adaptive systems are designed and evaluated in sustained human-AI interaction. In-situ longitudinal studies are needed to validate these findings in real-world contexts.
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