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：对话智能体越来越需要根据情境进行适应，并在交互中发展其个性，然而，大多数对话智能体一旦配置完成，其个性便保持静态不变。我们进行了一项探索性研究，探讨当智能体个性可动态调整时，用户期望如何形成和演变。为研究这一问题，我们设计了一个原型对话界面，使用户能够在信息性、情感性和评价性三个任务情境中，沿基于研究的八个维度调整智能体的个性。我们进行了一项在线混合方法研究，涉及60名参与者，采用潜在特征分析来表征人格类别，并使用轨迹分析来追踪人格调整的演变模式。这些方法揭示了初始和最终配置阶段的不同人格特征以及由情境敏感性塑造的调整轨迹。参与者还重视自主性，认为智能体更具拟人化，并报告了更高的信任度。我们的研究结果强调了设计能够与用户共同适应的对话智能体的重要性，从而推动更响应性和以人为中心的人工智能发展。