Caregivers often turn to online communities for informational and emotional support. In these spaces, peer supporters frequently draw on personal narratives to respond to emotionally complex caregiving situations. As LLMs are increasingly designed as peer-like sources of support, they introduce a critical tension: AI can provide immediate, private, and nonjudgmental support, but it cannot authentically possess the lived experiences that make human peer support meaningful. Yet, when prompted to sound peer-like, LLMs may generate language that implies lived experience. This creates a synthetic lived experience paradox: the same experiential language that may make AI support feel warm, relatable, and peer-like can also falsely position the system as someone with lived experience. We examine this paradox in the context of family caregivers of people living with Alzheimer's Disease and Related Dementias (ADRD). Drawing on caregiver support exchanges from online communities and prompted peer-like responses from three LLMs -- LLaMA, GPT-4o-mini, and MedGemma -- we analyze how human peers use personal narratives and how AI incorporates similar narrative forms. Psycholinguistic analysis shows that peer responses used significantly more first-person and past-focused language than peer-like AI responses. Qualitatively, we identify seven types of personal narratives in human peer support and show that AI often captures their emotional work, but can fabricate experiential grounding. These findings reveal a narrative authenticity gap: peer-like AI can generate synthetic lived experience without the real experience that makes peer support meaningful. We argue that caregiver-support AI systems need mechanisms to distinguish supportive peer-like framing from fabricated lived experience, ensuring that models can offer warmth and validation without falsely positioning themselves as experiential peers.

翻译：照护者常通过在线社区寻求信息和情感支持。在这些空间中，同伴支持者常借助个人叙事来回应情感复杂的照护情境。随着大语言模型被设计为同伴式支持来源，它们引入了一个关键矛盾：AI能提供即时、私密且无评判的支持，但无法真实拥有使人类同伴支持具有意义的生活经验。然而，当被要求模仿同伴语气时，LLMs可能生成暗示拥有生活经验的语言。这便产生了合成生活经验悖论：那些使AI支持显得温暖、可亲且同伴化的经验性语言，同时也可能错误地将系统定位为拥有生活经验的个体。我们以阿尔茨海默病及相关痴呆症（ADRD）患者的家庭照护者为背景考察这一悖论。通过分析从在线社区获取的照护者支持交流，以及三个LLM（LLaMA、GPT-4o-mini和MedGemma）生成的同伴式回答，我们研究了人类同伴如何使用个人叙事，以及AI如何融入类似的叙事形式。心理语言学分析表明，同伴回答相比同伴式AI回答使用了显著更多的第一人称和过去式语言。在定性层面，我们识别了人类同伴支持中的七类个人叙事，并发现AI常能捕捉其情感功能，但可能编造经验基础。这些发现揭示了一个叙事真实性鸿沟：同伴式AI能生成合成生活经验，却缺乏使同伴支持具有意义的真实经验。我们认为照护者支持AI系统需要建立机制，区分支持性同伴框架与编造的生活经验，确保模型在提供温暖与认同时不会错误地将自身定位为经验同伴。