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