Contextual AI Journaling: Integrating LLM And Time Series Behavioral Sensing Technology To Promote Self-reflection And Well-being Using The Mindscape App

Nepal Subigya, Pillai Arvind, Campbell William, Massachi Talie, Choi Eunsol Soul, Xu Orson, Kuc Joanna, Huckins Jeremy, Holden Jason, Depp Colin, Jacobson Nicholas, Czerwinski Mary, Granholm Eric, Campbell Andrew T.. Arxiv 2024

[Paper]    
Prompting RAG

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.

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