机器人比聊天机器人更能让用户持续敞开心扉，长期使用效果更好。
Engagement and Disclosures in LLM-Powered Cognitive Behavioral Therapy Exercises: A Factorial Design Comparing the Influence of a Robot vs. Chatbot Over Time
- 用因子设计对比实体机器人与聊天机器人的治疗效果
- 两周内机器人组参与度和情感披露量持续上升，聊天机器人组则下降
- 适合关注人机交互长期心理干预效果的研究者
为应对全球心理健康危机，研究者正开发基于大语言模型（LLM）的聊天机器人与社交助手机器人（SAR）以提升治疗可及性。然而，这些技术的长期影响仍不明确。本研究采用因子设计，评估实体化形态与互动时长对参与者披露行为的影响。26名大学生在两周内于住所完成每日认知行为疗法（CBT）练习，使用搭载LLM的SAR或无实体聊天机器人。分析每轮会话及随时间变化的主动参与度与高亲密性披露（观点、判断、情绪）。结果显示：在两个指标上，时间与实体化均存在显著交互效应——机器人组的参与度与情感披露随时间上升，而聊天机器人组则下降。
原文摘要 · Abstract (English)
Many researchers are working to address the worldwide mental health crisis by developing therapeutic technologies that increase the accessibility of care, including leveraging large language model (LLM) capabilities in chatbots and socially assistive robots (SARs) used for therapeutic applications. Yet, the effects of these technologies over time remain unexplored. In this study, we use a factorial design to assess the impact of embodiment and time spent engaging in therapeutic exercises on participant disclosures. We assessed transcripts gathered from a two-week study in which 26 university student participants completed daily interactive Cognitive Behavioral Therapy (CBT) exercises in their residences using either an LLM-powered SAR or a disembodied chatbot. We evaluated the levels of active engagement and high intimacy of their disclosures (opinions, judgments, and emotions) during each session and over time. Our findings show significant interactions between time and embodiment for both outcome measures: participant engagement and intimacy increased over time in the physical robot condition, while both measures decreased in the chatbot condition.
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