对话中的生活经验：共同设计大语言模型中的个性化以支持青少年心理健康
Lived Experience in Dialogue: Co-designing Personalization in Large Language Models to Support Youth Mental Well-being
AI总结：
本研究通过参与式设计探讨如何利用青少年的生活经验改进大语言模型的个性化，提出人本情境化、明确边界和对话式支架三个主题，并映射为可指导微调的设计特征，以提升青少年心理健康支持工具的契合度与有效性。
摘要：
青少年越来越多地求助于大语言模型（LLMs）来获得心理健康支持，然而当前LLMs中的个性化可能忽视塑造其需求的异质性生活经验。我们开展了一项参与式研究，邀请青少年、家长和青少年照护工作者（N=38），以共同创建的青少年角色画像（personas）作为支架，旨在收集社区对LLMs如何实现更有意义的个性化以支持青少年心理健康的观点。分析确定了三个主题：响应即时需求的人本情境化、围绕服务范围和线下转介的明确边界，以及促进反思与自主性的对话式支架。我们将这些主题映射到说服式设计特征上，涵盖任务建议、社会促进和系统可信度，并创建了相应的对话片段以指导LLM微调。我们的研究结果表明，生活经验可以被操作化，用以指导LLMs中的设计特征，从而增强基于LLM的干预措施与青少年及其社区现实情况的契合度，有助于开发更有效个性化的数字健康工具。
英文摘要：
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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