Peer-run organizations (PROs) provide critical, recovery-based behavioral health support rooted in lived experience. As large language models (LLMs) enter this domain, their scale, conversationality, and opacity introduce new challenges for situatedness, trust, and autonomy. Partnering with Collaborative Support Programs of New Jersey (CSPNJ), a statewide PRO in the Northeastern United States, we used comicboarding, a co-design method, to conduct workshops with 16 peer specialists and 10 service users exploring perceptions of integrating an LLM-based recommendation system into peer support. Findings show that depending on how LLMs are introduced, constrained, and co-used, they can reconfigure in-room dynamics by sustaining, undermining, or amplifying the relational authority that grounds peer support. We identify opportunities, risks, and mitigation strategies across three tensions: bridging scale and locality, protecting trust and relational dynamics, and preserving peer autonomy amid efficiency gains. We contribute design implications that center lived-experience-in-the-loop, reframe trust as co-constructed, and position LLMs not as clinical tools but as relational collaborators in high-stakes, community-led care.

翻译：同伴主导组织（PROs）基于亲身经历，提供关键且以康复为导向的行为健康支持。随着大型语言模型（LLMs）进入这一领域，其规模性、对话性和不透明性为情境性、信任与自主性带来了新的挑战。我们与美国东北部全州性的同伴主导组织——新泽西州协作支持计划（CSPNJ）合作，采用漫画板这一协同设计方法，与16名同伴专家和10名服务使用者开展了研讨会，探讨将基于LLM的推荐系统整合到同伴支持中的看法。研究发现，根据LLMs的引入方式、约束条件及协同使用模式，它们可能通过维持、削弱或放大同伴支持所依赖的关系权威，从而重构现场互动动态。我们围绕三组矛盾识别了机遇、风险与缓解策略：连接规模性与地方性、保护信任与关系动态，以及在效率提升中维护同伴自主性。我们提出了以“亲身经验在环”为核心的设计启示，将信任重构为共同构建的过程，并将LLMs定位为高风险、社区主导的照护中的关系协作者，而非临床工具。