探索基于大语言模型的工具以促进社交焦虑的意象暴露练习
Explore LLM-enabled Tools to Facilitate Imaginal Exposure Exercises for Social Anxiety
AI总结：
本文探讨了基于大语言模型的工具在社交焦虑意象暴露练习中的可行性，通过与心理健康专业人员合作设计工具，并进行用户研究，验证了该工具在生成个性化暴露脚本方面的有效性及适应性。
摘要：
社交焦虑（SA）是一种普遍的心理健康挑战，严重影响日常社交互动。意象暴露（IE），一种认知行为疗法（CBT）技术，涉及想象引发焦虑的情境，但因传统IE作业要求患者构建和维持临床相关的恐惧叙述而使用受限。本文探讨了基于大语言模型（LLM）的工具在支持IE中的可行性，通过与心理健康专业人员共同设计ImaginalExpoBot，随后进行形式评价和用户研究。研究发现，LLM支持可帮助准备引发焦虑的情境，同时允许即时、用户特定的适应，使情景保持在治疗有益的“容忍窗口”内。参与者和心理健康专业人员也指出了连续性和定制化的局限性，提示未来设计需要更深入的适应性。这些发现为将LLM整合到结构化治疗实践中提供了初步设计见解，以可及和可扩展的方式实现。
英文摘要：
Social anxiety (SA) is a prevalent mental health challenge that significantly impacts daily social interactions. Imaginal Exposure (IE), a Cognitive Behavioral Therapy (CBT) technique involving imagined anxiety-provoking scenarios, is effective but underutilized, in part because traditional IE homework requires clients to construct and sustain clinically relevant fear narratives. In this work, we explore the feasibility of an LLM-enabled tool that supports IE by generating vivid, personalized exposure scripts. We first co-designed ImaginalExpoBot with mental health professionals, followed by a formative evaluation with five therapists and a user study involving 19 individuals experiencing SA symptoms. Our findings show that LLM-enabled support can facilitate preparation for anxiety-inducing situations while enabling immediate, user-specific adaptation, with scenarios remaining within a therapeutically beneficial "window of tolerance". Our participants and MHPs also identified limitations in continuity and customization, pointing to the need for deeper adaptivity in future designs. These findings offer preliminary design insights for integrating LLMs into structured therapeutic practices in accessible, scalable ways.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
