用大模型模拟康复者聊天机器人，助力进食障碍患者心理恢复
Collective Voice: Recovered-Peer Support Mediated by An LLM-Based Chatbot for Eating Disorder Recovery
- 设计一个以康复者身份的聊天机器人，模拟真实康复者叙事
- 26名患者参与实验，该机器人引发更强情感共鸣
- 适合心理健康类AI产品设计，尤其关注角色可信度
同伴康复叙事在进食障碍（ED）康复中能提供专业或普通指导无法替代的支持，如激发希望与持续恢复。然而，此类支持受限于康复同伴资源稀缺及对康复者自身可能带来的复发风险。为此，我们设计了RecoveryTeller——一款采用康复者人设的大语言模型聊天机器人，宣称自己已从进食障碍中康复。通过一项为期20天的交叉实验（26名参与者，每人使用两个聊天机器人各10天），我们对比了该机器人与仅具普通指导背景的非康复者人设聊天机器人的效果。结果发现，RecoveryTeller引发了更强的情感共鸣，但因情感信任与认知信任之间的张力，用户认为两者应互补而非替代。研究为心理健康聊天机器人的人物设定提供了设计启示。
原文摘要 · Abstract (English)
Peer recovery narratives provide unique benefits beyond professional or lay mentoring by fostering hope and sustained recovery in eating disorder (ED) contexts. Yet, such support is limited by the scarcity of peer-involved programs and potential drawbacks on recovered peers, including relapse risk. To address this, we designed RecoveryTeller, a chatbot adopting a recovered-peer persona that portrays itself as someone recovered from an ED. We examined whether such a persona can reproduce the support affordances of peer recovery narratives. We compared RecoveryTeller with a lay-mentor persona chatbot offering similar guidance but without a recovery background. We conducted a 20-day cross-over deployment study with 26 ED participants, each using both chatbots for 10 days. RecoveryTeller elicited stronger emotional resonance than a lay-mentor chatbot, yet tensions between emotional and epistemic trust led participants to view the two personas as complementary rather than substitutes. We provide design implications for mental health chatbot persona design.
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