ExploreSelf: Fostering User-driven Exploration and Reflection on Personal Challenges with Adaptive Guidance by Large Language Models

要旨

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.

著者
Inhwa Song
KAIST, Daejeon, Korea, Republic of
SoHyun Park
NAVER Cloud, Seongnam, Korea, Republic of
Sachin R. Pendse
Georgia Institute of Technology, Atlanta, Georgia, United States
Jessica Lee. Schleider
Northwestern University, Evanston, Illinois, United States
Munmun De Choudhury
Georgia Institute of Technology, Atlanta, Georgia, United States
Young-Ho Kim
NAVER AI Lab, Seongnam, Gyeonggi, Korea, Republic of
DOI

10.1145/3706598.3713883

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713883

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Digital Health and Well-being

G302
6 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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