A Comparative Evaluation of Human and LLM-Based Counselors: Trust, User Experience, and Design Implications

Abstract

Large language models (LLMs) have expanded the potential of conversational AI in mental health support, yet counseling inherently relies on trust and relational aspects that may not transfer directly to these systems. We examine how users’ trust and experiences differ between human and LLM-based counseling, conducting a within-subjects study in which participants discussed their own concerns with both a licensed human counselor and an LLM-based counselor through text-based sessions. We assessed subjective distress and trust across five dimensions, and analyzed open-ended feedback across different trust profiles. The human counselor condition received higher overall trust, with the largest gaps in Faith and Personal Attachment, while Understandability remained comparable across conditions. Participants also reported greater reductions in subjective distress following human counselor sessions. Qualitative analysis further revealed that empathy, exploratory questioning, contextual understanding, and personalization were key factors shaping users’ trust, suggesting design implications for LLM-based counseling systems.