让聊天机器人根据情境动态调整人设和性格强度，提升用户信任与体验。
Behavior-Adaptive Conversational Agents: Toward a Fluid Personality Framework
- 根据任务、用户目标和紧急程度，动态切换角色与性格表达
- 中等性格强度比极端高低表现更优，提升信任与采纳意愿
- 适合医疗咨询、健身教练等需要灵活应变的场景
基于大语言模型的对话智能体已广泛应用，为人工智能驱动的行为改变带来新机遇。其投射细腻人格与多样隐喻角色的能力引发一个设计问题：如何根据当下情境校准代理的人格与风格？最新证据表明，（1）中等程度的人格表达在目标导向任务中优于过低或过高极端，能提升信任度、愉悦感与采纳意愿；（2）符合语境的隐喻角色优于固定单一模式的助手，在用户体验与接受度上表现更佳。然而多数对话智能体仍固定人设与风格，当情境、紧迫性与正式程度变化时易产生错配，例如在医疗信息查询、健身指导和反思学习场景中。本文提出一种流体人格框架，联合适应（1）代理的隐喻角色（如教练、导师、图书管理员或工具），以及（2）人格表达强度（低、中、高），依据任务上下文、用户目标与特质、情境紧迫性进行动态调节。文章勾勒了该框架及其核心设计维度。
原文摘要 · Abstract (English)
Large language model (LLM)-based conversational agents (CAs) are now ubiquitous, creating new opportunities for AI-mediated behavior change. Their capacity to project nuanced personalities and adopt diverse metaphorical roles raises a design question: how should an agent's persona and personality be calibrated to the moment? Recent evidence suggests that (i) moderate personality expression outperforms low or high extremes on trust, enjoyment, and intention to adopt in goal-oriented tasks, and (ii) context-appropriate metaphors outperform static one-note assistants on user experience and uptake. Yet most CAs still fix both persona and style, risking misalignment when dynamics, urgency, and formality vary, for example in medical information seeking, fitness coaching, and reflective learning. We propose a Fluid Personality Framework that jointly adapts (1) the agent's metaphorical persona, such as coach, tutor, librarian, or tool, and (2) its personality expression intensity, low, medium, or high, as a function of task context, user goals and traits, and situational urgency. We sketch the framework and its core design dimensions.
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