Human-AI Collaborative Journaling with POCKET-MIND: A Dual-Prompt Framework for Emotional Exploration and Goal Attainment

Human-AI collaborative systems are increasingly explored as tools for promoting mental well-being and supporting personal development. We present POCKET-MIND, a personalized digital journaling system powered by a Large Language Model (LLM) that facilitates both emotional exploration and goal pursuit through a novel Dual-Prompt Framework. Unlike traditional journaling apps that treat emotional reflection and goal tracking as separate tasks, POCKET-MIND integrates these dimensions by generating adaptive prompts that help users meaningfully connect their feelings with their personal aspirations. In a one-week exploratory study with 30 young adults, preliminary findings suggest that POCKET-MIND may support emotional articulation, self-reflection, and goal-directed behaviors. While the study had a relatively small sample size, the findings highlight the potential of Human-AI collaborative journaling for personal mental health support. This work contributes to Human-Computer Interaction (HCI) by offering early design insights into adaptive conversational systems that personalize reflective practices and foster user growth through interactive experiences.

人机协同系统正日益被开发为促进心理健康、支持个人成长的工具。本研究提出POCKET-MIND：一款由大语言模型（Large Language Model, LLM）驱动的个性化数字日记系统，其通过创新的双提示框架（Dual-Prompt Framework），助力用户开展情绪探索与目标追求。不同于将情绪反思与目标追踪割裂为两项独立任务的传统日记应用，POCKET-MIND通过生成自适应提示，将这两个维度有机融合，帮助用户将自身感受与个人抱负建立有意义的联结。在一项针对30名年轻人开展的为期一周的探索性研究中，初步结果显示，POCKET-MIND能够支持用户清晰表达情绪、开展自我反思并实施目标导向行为。尽管本次研究的样本量相对较小，但研究结果凸显了人机协同日记系统在个人心理健康支持领域的应用潜力。本研究针对可通过交互体验实现个性化反思实践、助力用户成长的自适应对话系统提供了早期设计洞察，为人机交互（Human-Computer Interaction, HCI）领域贡献了新的研究成果。