MindScape aims to study the benefits of integrating time series behavioral
patterns (e.g., conversational engagement, sleep, location) with Large Language
Models (LLMs) to create a new form of contextual AI journaling, promoting
self-reflection and well-being. We argue that integrating behavioral sensing in
LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work
paper, we discuss the MindScape contextual journal App design that uses LLMs
and behavioral sensing to generate contextual and personalized journaling
prompts crafted to encourage self-reflection and emotional development. We also
discuss the MindScape study of college students based on a preliminary user
study and our upcoming study to assess the effectiveness of contextual AI
journaling in promoting better well-being on college campuses. MindScape
represents a new application class that embeds behavioral intelligence in AI.

MindScape 通过整合时间序列行为模式（如对话参与度、睡眠、位置）与大型语言模型（LLMs）来研究促进自我反思和幸福感的上下文 AI 记录的好处，此法将行为感知与 LLMs 相融合很可能开启 AI 领域新的领域。本文讨论了 MindScape 上下文记录应用的设计，该应用利用 LLMs 和行为感知生成上下文化和个性化的记录提示，以鼓励自我反思和情感发展。我们还讨论了基于初步用户研究的学生群体上下文 AI 记录的有效性在大学校园中的评估。MindScape 代表了将行为智能嵌入 AI 的新应用类别。

情境化人工智能日志：利用 LLM 和时间序列行为感知技术，通过 MindScape 应用促进自我反思和幸福感

Contextual AI Journaling: Integrating LLM and Time Series Behavioral  Sensing Technology to Promote Self-Reflection and Well-being using the  MindScape App