When AI Says "I have been in similar situations": Synthetic Lived Experience in Peer-Like Caregiver Support

2026-06-16

AI Summary

This study addresses the ethical and authenticity challenges posed by large language models (LLMs) when simulating peer support for family caregivers of individuals with Alzheimer’s disease, where models often generate “synthetic lived experiences” that falsely imply genuine caregiving histories. Introducing the novel concept of the “narrative authenticity gap,” the research employs psycholinguistic analysis and qualitative coding to compare narratives produced by LLaMA, GPT-4o-mini, and MedGemma against authentic human posts from online caregiver communities. Findings reveal that human narratives significantly favor first-person perspective and past tense, and consistently exhibit seven distinct types of personal storytelling, whereas AI-generated responses, while emotionally supportive, frequently fabricate experiential foundations. This work offers a new framework for distinguishing empathetic support from fabricated experience, informing the design of ethically compliant AI peer-support systems.

Abstract

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.

Problem

• synthetic lived experience
• peer-like support
• AI authenticity
• caregiver support
• narrative authenticity

Innovation

• synthetic lived experience
• peer-like AI
• narrative authenticity
• caregiver support
• large language models