Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App
<|reference_start|>Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App: 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.<|reference_end|>
@article{nepal2024contextual,
    title={Contextual AI Journaling: Integrating LLM and Time Series Behavioral
  Sensing Technology to Promote Self-Reflection and Well-being using the
  MindScape App},
    author={Subigya Nepal, Arvind Pillai, William Campbell, Talie Massachi, Eunsol
  Soul Choi, Orson Xu, Joanna Kuc, Jeremy Huckins, Jason Holden, Colin Depp,
  Nicholas Jacobson, Mary Czerwinski, Eric Granholm, Andrew T. Campbell},
    journal={arXiv preprint arXiv:2404.00487},
    year={2024},
    doi={10.1145/3613905.3650767},
    archivePrefix={arXiv},
    eprint={2404.00487},
    primaryClass={cs.HC cs.AI}
}