用行为数据+大模型打造个性化心理日记，提升大学生心理健康。
MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences
- 结合睡眠、位置等行为数据与大语言模型生成定制日记提示。
- 8周干预后积极情绪提升7%，焦虑抑郁评分周均下降0.25。
- 适合关注校园心理支持与智能健康应用的研究者和开发者。
大学生心理健康问题普遍，亟需促进自我觉察与整体福祉的有效干预。MindScape 提出一种创新的 AI 驱动日记方法，融合被动采集的对话参与度、睡眠模式和位置等行为数据与大型语言模型（LLMs）。该整合实现高度个性化且具备上下文感知的日记体验，通过嵌入行为智能增强自我觉察与幸福感。我们开展了为期8周的探索性研究，涉及20名大学生，结果表明，MindScape 应用显著提升积极情绪（+7%）、降低消极情绪（-11%）、孤独感（-6%）以及焦虑与抑郁水平，PHQ-4评分呈现显著的周间下降趋势（系数 -0.25），同时正念（+7%）与自我反思能力（+6%）亦有提升。研究凸显情境化AI日记的优势，参与者特别赞赏应用提供的个性化提示与洞察。分析还对比了情境化与通用提示的响应差异，提炼出用户反馈及在高校推广情境化AI日记以改善心理健康的策略。本研究为情境化AI日记在心理健康领域的应用提供了初步实证基础。
原文摘要 · Abstract (English)
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.
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