"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions

[2506.09354] "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Human-Computer Interaction arXiv:2506.09354 (cs) [Submitted on 11 Jun 2025 ( v1 ), last revised 22 Jun 2026 (this version, v4)] Title: "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions Authors: Kellie Yu Hui Sim , Roy Ka-Wei Lee , Kenny Tsu Wei Choo View a PDF of the paper titled "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions, by Kellie Yu Hui Sim and 2 other authors View PDF HTML (experimental) Abstract: Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. \emph{Peer support}, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present and evaluate an AI-supported system with an LLM-simulated distressed client (\client{}), context-sensitive LLM-generated suggestions (\suggestions{}), and real-time emotion visualisations. 2 mixed-methods studies with 12 peer supporters and 6 mental health professionals (i.e., experts) examined the system's effectiveness and implications for practice. Both groups recognised its potential to enhance training and improve interaction quality. However, we found a key tension emerged: while peer supporters engaged meaningfully, experts consistently flagged critical issues in peer supporter responses, such as missed distress cues and premature advice-giving. This misalignment highlights potential limitations in current peer support training, especially in emotionally charged contexts where safety and fidelity to best practices are essential. Our findings underscore the need for standardised, psychologically grounded training, especially as peer support scales globally. They also demonstrate how LLM-supported systems can scaffold this development--if designed with care and guided by expert oversight. This work contributes to emerging conversations on responsible AI integration in mental health and the evolving role of LLMs in augmenting peer-delivered care. Comments: Accepted at CSCW 2026. 53 pages, 12 figures, 17 tables Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI) ACM classes: H.5.0 Cite as: arXiv:2506.09354 [cs.HC] (or arXiv:2506.09354v4 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2506.09354 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3816956 Focus to learn more DOI(s) linking to related resources Submission history From: Kellie Yu Hui Sim [ view email ] [v1] Wed, 11 Jun 2025 03:06:41 UTC (8,054 KB) [v2] Wed, 1 Apr 2026 11:06:04 UTC (6,681 KB) [v3] Wed, 8 Apr 2026 16:47:27 UTC (6,681 KB) [v4] Mon, 22 Jun 2026 05:18:54 UTC (6,687 KB) Full-text links: Access Paper: View a PDF of the paper titled "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions, by Kellie Yu Hui Sim and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2025-06 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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