用自适应提问帮助用户自主探索心理挑战，提升反思深度。
ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models
- 基于大模型动态生成问题，支持用户自主引导反思过程。
- 19人研究显示，灵活引导显著提升参与度与洞察力。
- 适合心理健康应用、个人成长工具开发者参考。
用文字表达压力经历已被证明有助于改善身心健康，但人们常因难以组织思绪和情绪而放弃写作干预。已有研究使用反思性提示提供方向，大语言模型（LLMs）也展现出提供个性化指导的潜力。然而，现有系统往往限制了用户的自主性。为此，我们提出ExploreSelf，一个由大语言模型驱动的应用，旨在赋能用户自主掌控反思旅程，并通过动态生成的问题提供自适应支持。通过对19名参与者进行探索性研究，我们考察了用户如何在ExploreSelf中探索并反思个人挑战。研究发现，参与者高度认可灵活导航带来的自适应引导，有效提升了反思的深度与投入度。基于这些发现，我们探讨了设计以用户为中心、高效促进个人挑战反思的LLM驱动工具的潜在意义。
原文摘要 · Abstract (English)
Expressing stressful experiences in words is proven to improve mental and physical health, but individuals often disengage with writing interventions as they struggle to organize their thoughts and emotions. Reflective prompts have been used to provide direction, and large language models (LLMs) have demonstrated the potential to provide tailored guidance. However, current systems often limit users' flexibility to direct their reflections. We thus present ExploreSelf, an LLM-driven application designed to empower users to control their reflective journey, providing adaptive support through dynamically generated questions. Through an exploratory study with 19 participants, we examine how participants explore and reflect on personal challenges using ExploreSelf. Our findings demonstrate that participants valued the flexible navigation of adaptive guidance to control their reflective journey, leading to deeper engagement and insight. Building on our findings, we discuss the implications of designing LLM-driven tools that facilitate user-driven and effective reflection of personal challenges.
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