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

• Main Authors: Song, Inhwa, Park, SoHyun, Pendse, Sachin R, Schleider, Jessica Lee, De Choudhury, Munmun, Kim, Young-Ho
• Format: Journal Article
• Language: English
• Published: 15-09-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.2409.09662

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.