Conversational agents are increasingly expected to adapt across contexts and evolve their personalities through interactions, yet most remain static once configured. We present an exploratory study of how user expectations form and evolve when agent personality is made dynamically adjustable. To investigate this, we designed a prototype conversational interface that enabled users to adjust an agent's personality along eight research-grounded dimensions across three task contexts: informational, emotional, and appraisal. We conducted an online mixed-methods study with 60 participants, employing latent profile analysis to characterize personality classes and trajectory analysis to trace evolving patterns of personality adjustment. These approaches revealed distinct personality profiles at initial and final configuration stages, and adjustment trajectories, shaped by context-sensitivity. Participants also valued the autonomy, perceived the agent as more anthropomorphic, and reported greater trust. Our findings highlight the importance of designing conversational agents that adapt alongside their users, advancing more responsive and human-centred AI.

翻译：对话智能体日益被期望能够适应不同情境并通过交互演化其人格特征，然而大多数系统一旦配置完成即保持静态。本研究探索了当智能体人格被设计为动态可调节时，用户期望如何形成与演化。为此，我们设计了一个原型对话界面，允许用户在三种任务情境（信息型、情感型、评价型）中，沿八个研究验证的维度调节智能体人格。我们通过线上混合方法研究收集了60名参与者的数据，采用潜在剖面分析来刻画人格类别，并运用轨迹分析追踪人格调节的演化模式。这些方法揭示了在初始与最终配置阶段存在不同的人格剖面，以及由情境敏感性塑造的调节轨迹。参与者重视这种自主调节机制，感知到智能体更具拟人化特征，并表现出更高的信任度。我们的研究结果凸显了设计能够与用户共同适应的对话智能体的重要性，为推进更具响应性和以人为中心的人工智能提供了新方向。