Seek and De-Stress: Matching Supporter Identity to Seeker Need in AI Emotional Support

Abstract

People in distress are turning to empathetic-sounding conversational AI, but a response that sounds caring may still miss what a specific person actually needs. We ask whether support improves when simulated help-seekers can choose among differentiated supporter identities rather than speak with one generic assistant. We developed 42 help-seeking personas grounded in real pandemic-era mental health data (COVIDiSTRESS) and drew on separate psychometric and linguistic sources to create four synthetic supporter identities with distinct support philosophies. Each help-seeker completed an intake battery, which suggested two broad need patterns: emotional steadiness and practical direction. These groups did not prefer the same supporters. Help-seekers ranked the four supporters using text descriptions and textual, non-rendered avatar descriptions, then had conversations with their top choice, bottom choice, and a generic baseline. We ran this across four commercial model providers, yielding 1,008 conversations. When paired with their top-ranked supporter, simulated help-seekers more often chose to prolong the conversation, reported greater relief, and felt more understood than they did with the default assistant. Process audits showed that matched supporters were not simply warmer: they scored higher on problem-solving and personalization, while generic or unmatched support often sounded more validating. Identity-grounded supporters also produced fewer coded markers of sycophancy and system misrepresentation than default assistants in three of four providers. The findings suggest that fit, helpfulness, and structural safety are separable design problems, and that coherent supporter identity can help address all three.