探索自我:通过大型语言模型的自适应指导,促进用户对个人挑战进行探索和反思

Inhwa Song, SoHyun Park, Sachin R. Pendse, Jessica Lee Schleider, Munmun De Choudhury, Young-Ho Kim
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引用次数: 0

摘要

事实证明,用语言表达压力体验可以改善身心健康,但个人往往会因为难以组织自己的思想和情绪而放弃写作干预。反思性提示已被用于提供指导,而大型语言模型(LLM)已证明了提供定制指导的潜力。目前的系统往往限制了用户引导反思的灵活性。因此,我们提出了探索自我(ExploreSelf),这是一个由 LLM 驱动的应用程序,旨在让用户有能力控制自己的反思之旅。ExploreSelf允许用户通过动态生成的问题获得自适应支持。通过对 19 名参与者的探索性研究,我们考察了参与者如何使用 ExploreSelf 探索和反思个人挑战。我们的研究结果表明,参与者非常重视在指导支持和自由控制反思旅程之间取得平衡,从而获得更深的参与感和洞察力。在研究结果的基础上,我们讨论了设计以 LLM 为驱动的工具的意义,这些工具通过有效的反思实践促进了用户赋权。
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ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models
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. 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. ExploreSelf allows users to receive 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 balance between guided support and freedom to control their reflective journey, leading to deeper engagement and insight. Building on our findings, we discuss implications for designing LLM-driven tools that promote user empowerment through effective reflective practices.
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