- Nepal, Subigya;
- Pillai, Arvind;
- Campbell, William;
- Massachi, Talie;
- Heinz, Michael;
- Kunwar, Ashmita;
- Choi, Eunsol;
- Xu, Xuhai;
- Kuc, Joanna;
- Huckins, Jeremy;
- Holden, Jason;
- Preum, Sarah;
- Depp, Colin;
- Jacobson, Nicholas;
- Czerwinski, Mary;
- Granholm, Eric;
- Campbell, Andrew
Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores 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 apps 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). 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.