通过长期分析人机对话 informing 机器人幸福感教练设计
Informing Robot Wellbeing Coach Design through Longitudinal Analysis of Human-AI Dialogue
AI总结：
本文通过分析长期人机对话数据，揭示用户与AI幸福感教练互动的动态模式，为设计支持用户自主性和伦理边界的新型机器人幸福感教练提供指导。
摘要：
社交机器人和对话代理正被探索作为支持幸福感、目标设定和日常自我调节的支持。尽管先前工作强调了它们激励和引导用户潜在的能力，但大部分证据依赖于自我报告结果或短期、研究人员主导的接触。因此，我们对人们在真实世界情境中使用此类系统时展开的互动动态了解甚少，以及这些动态应如何塑造未来机器人幸福感教练。本文通过分析38名大学生与基于LLM的幸福感教练之间纵向交换的4352条消息，填补了这一空白。我们的结果提供了对用户如何自然地塑造、引导甚至有时在支持性的人机对话中挣扎的细致视角，揭示了用户主导的方向、寻求指导和情感表达的模式。我们讨论了这些动态如何影响设计机器人幸福感教练，以支持用户自主性，提供适当的支架，并在持续的幸福感互动中维护伦理界限。
英文摘要：
Social robots and conversational agents are being explored as supports for wellbeing, goal-setting, and everyday self-regulation. While prior work highlights their potential to motivate and guide users, much of the evidence relies on self-reported outcomes or short, researcher-mediated encounters. As a result, we know little about the interaction dynamics that unfold when people use such systems in real-world contexts, and how these dynamics should shape future robot wellbeing coaches. This paper addresses this gap through content analysis of 4352 messages exchanged longitudinally between 38 university students and an LLM-based wellbeing coach. Our results provide a fine-grained view into how users naturally shape, steer, and sometimes struggle within supportive human-AI dialogue, revealing patterns of user-led direction, guidance-seeking, and emotional expression. We discuss how these dynamics can inform the design of robot wellbeing coaches that support user autonomy, provide appropriate scaffolding, and uphold ethical boundaries in sustained wellbeing interactions.
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。
