Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. 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, context-sensitive LLM-generated suggestions, and real-time emotion visualisations. 2 mixed-methods studies with 12 peer supporters and 5 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.

翻译：心理健康日益成为全球性关切，这激发了人们利用人工智能驱动解决方案来扩大心理社会支持可及性的兴趣。源于亲身经历的同伴支持，是专业护理的宝贵补充。然而，培训、有效性和定义上的差异引发了对质量、一致性和安全性的担忧。大型语言模型（LLMs）为增强同伴支持互动提供了新机遇，尤其在实时的基于文本的交互中。我们提出并评估了一个人工智能辅助系统，该系统包含LLM模拟的处于困扰中的客户、上下文敏感的LLM生成建议，以及实时情绪可视化。通过两项混合方法研究，涉及12名同伴支持者和5名心理健康专业人士（即专家），我们考察了该系统的有效性及对实践的启示。两组参与者都认可其在提升培训质量和改善互动效果方面的潜力。然而，我们发现了一个关键张力：尽管同伴支持者进行了有意义的互动，但专家始终指出同伴支持者回应中的关键问题，例如遗漏痛苦信号和过早提供建议。这种错位凸显了当前同伴支持培训的潜在局限性，尤其是在那些安全性和遵循最佳实践至关重要的情感高负荷情境中。我们的研究结果强调了进行标准化、以心理学为基础的培训的必要性，特别是在同伴支持在全球范围内扩展的背景下。同时，研究也展示了LLM辅助系统如何能够支撑这一发展——前提是精心设计并接受专家监督。这项工作为关于心理健康领域负责任地整合人工智能以及LLM在增强同伴主导护理中不断演变的角色等新兴讨论做出了贡献。