通过多模块系统驱动的具身对话代理探究AI在同伴支持中的应用
Investigating AI in Peer Support via Multi-Module System-Driven Embodied Conversational Agents
AI总结：
本文通过多模块系统驱动的具身对话代理，探讨了AI在同伴支持中的应用，评估了基于CBT的LLM系统在心理健康支持中的有效性与用户感知。
AI中文摘要：
青少年的心理健康是全球关注的问题，同伴支持在日常情绪调节中发挥着关键作用。对话代理正日益被视为提供可及、个性化同伴支持的有前途的工具，尤其是在专业咨询有限的情况下。然而，现有系统往往存在输入格式僵化、预设响应和情感敏感性有限的问题。大型语言模型的出现为生成灵活、情境感知和富有同理心的响应提供了新可能。为了探索具有心理训练的个体如何感知此类系统在同伴支持情境中的表现，我们开发了一个基于LLM的多模块系统，以认知行为疗法（CBT）为指导，驱动具身对话代理。在一项用户研究（N=10）中，我们对参与者的态度进行了定性分析，重点关注信任度、响应质量、工作流程整合以及未来心理健康支持系统的潜在设计机会。
英文摘要：
Young people's mental well-being is a global concern, with peer support playing a key role in daily emotional regulation. Conversational agents are increasingly viewed as promising tools for delivering accessible, personalised peer support, particularly where professional counselling is limited. However, existing systems often suffer from rigid input formats, scripted responses, and limited emotional sensitivity. The emergence of large language models introduces new possibilities for generating flexible, context-aware, and empathetic responses. To explore how individuals with psychological training perceive such systems in peer support contexts, we developed an LLM-based multi-module system to drive embodied conversational agents informed by Cognitive Behavioral Therapy (CBT). In a user study (N=10), we qualitatively examined participants' perceptions, focusing on trust, response quality, workflow integration, and design opportunities for future mental well-being support systems.
