Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App (2404.00487v1)

Abstract: MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with 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.

• The paper demonstrates how integrating LLMs with time-series behavioral sensing in the MindScape app enables dynamic, context-aware journaling for self-reflection.
• It employs passively captured data from mobile sensors to generate personalized prompts that align with user activities and behaviors.
• The study shows promising implications for enhancing mental well-being and advancing human-computer interaction through AI-driven interventions.

Integrating Behavioral Sensing and LLMs in AI Journaling for Mental Well-being: A Study with the MindScape App

The paper examines the potential of combining LLMs with time-series behavioral sensing technologies to enhance self-reflection and well-being through contextual journaling using the MindScape app. The research primarily targets college students, acknowledging the significant mental health challenges prevalent within this demographic, as it leverages ubiquitous computing to integrate AI and behavioral intelligence into daily journaling practices.

The central thesis argues for an innovative application paradigm where AI applications, particularly those driven by mobile sensing, are enhanced through the seamless integration of time-series data. The application, aptly named MindScape, utilizes Android device sensors to capture user data, which includes metrics related to physical activity, social interactions, and location. The data acts as a foundation for generating personalized journaling prompts via LLMs, aiming to mitigate the introspective and memory recall limitations often encountered during self-reflection.

The researchers have conducted an exploratory qualitative paper with undergraduate participants to gauge typical journaling habits and the reception of context-sensitive prompts. According to the users' feedback, students expressed preferences for customized prompts that align with their digital engagement and daily experiences. These insights significantly influenced the app's design, ensuring the journaling experience is tailored to reflect individual behavioral contexts, ultimately enhancing potential positive mental health outcomes.

A salient aspect of their methodology includes the deployment of context-aware check-ins facilitated by a semantic map of campus locations and LLM-generated personalized prompts. This bi-directional relationship encourages not only introspection during journaling but also immediate, short-form reflection through timely behavioral nudges. Emotional regulation, self-awareness, and mindfulness form the core dimensions assessed by standardized questionnaires to capture the app’s impact accurately.

The implications of this paper are twofold, affecting both practical and theoretical dimensions of human-computer interaction (HCI) and AI development. Practically, the research highlights how context-aware journaling engineered through LLMs and passive mobile sensing can adapt to and support the fast-paced environments typical in collegiate settings. Theoretically, it signifies a push towards a new genre of AI, one that is intricately woven into user behaviors and attitudes, offering real-time, personalized mental health support.

Moving forward, the scalability of integrating AI with behavioral data offers substantial potential for personalized digital mental health interventions beyond academia. Expanding upon these methodologies, future research could explore scalability and further integration with more advanced multimodal data sources, potentially enabling broader deployment across diverse populations and settings. As AI continues to evolve, fostering applications that facilitate reflective practices supports the dual aims of advancing technological capacities and enhancing user well-being, thus sustaining the broader dialogue within the HCI and UbiComp communities.