从固定到灵活：在情境敏感交互中塑造AI个性
From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
AI总结：
研究探讨用户期望如何在动态调整AI个性时形成和演变，通过混合方法研究发现，情境敏感性影响个性配置轨迹，提升用户信任和拟人化感知。
摘要：
对话代理越来越多地被期望在不同情境中适应并通过交互演变其个性，但大多数代理一旦配置后仍保持静态。本文通过设计一个原型对话界面，使用户能沿八个研究基础维度调整代理的个性，跨信息、情感和评估三种任务情境进行研究。通过在线混合方法研究，60名参与者使用潜在类别分析确定个性类别，并通过轨迹分析追踪个性调整的演变模式。这些方法揭示了初始和最终配置阶段的个性特征差异以及受情境敏感性影响的调整轨迹。参与者重视自主性，认为代理更具拟人化特征，并报告了更高的信任度。研究结果强调了设计能与用户同步适应的对话代理的重要性，推动更响应性和以人类为中心的AI发展。
英文摘要：
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.
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。
