让青少年亲历经验指导大模型个性化，提升心理健康支持实效。
Lived Experience in Dialogue: Co-designing Personalization in Large Language Models to Support Youth Mental Well-being
- 通过共创青年角色画像，挖掘真实需求场景。
- 提炼三类关键设计特征：即时响应、边界清晰、促进反思。
- 适合心理支持类AI系统开发者参考落地。
越来越多青少年借助大语言模型（LLMs）寻求心理健康支持，但现有个性化功能常忽视其多元生活经历的影响。本研究联合38名青少年、家长及青年工作者开展参与式设计，以共创的青年角色画像为支架，收集社区视角，探讨如何使LLMs实现更有意义的个性化支持。分析识别出三大主题：贴合个体情境、响应即时需求；明确服务边界并引导线下转介；通过对话引导反思与自主性。研究将这些主题映射至任务建议、社交促进和系统可信度等说服性设计特征，并生成相应对话样本，用于指导LLM微调。结果表明，将真实生活经验转化为设计要素，可显著提升基于LLM的心理干预与青年及其社区现实的契合度，推动更有效个性化的数字健康工具发展。
原文摘要 · Abstract (English)
Youth increasingly turn to large language models (LLMs) for mental well-being support, yet current personalization in LLMs can overlook the heterogeneous lived experiences shaping their needs. We conducted a participatory study with youth, parents, and youth care workers (N=38), using co-created youth personas as scaffolds, to elicit community perspectives on how LLMs can facilitate more meaningful personalization to support youth mental well-being. Analysis identified three themes: person-centered contextualization responsive to momentary needs, explicit boundaries around scope and offline referral, and dialogic scaffolding for reflection and autonomy. We mapped these themes to persuasive design features for task suggestions, social facilitation, and system trustworthiness, and created corresponding dialogue extracts to guide LLM fine-tuning. Our findings demonstrate how lived experience can be operationalized to inform design features in LLMs, which can enhance the alignment of LLM-based interventions with the realities of youth and their communities, contributing to more effectively personalized digital well-being tools.
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