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
Dartmouth College, Hanover, New Hampshire, USA.
Colby College, Waterville, Maine, USA.
Ext Abstr Hum Factors Computing Syst. 2024 May;2024. doi: 10.1145/3613905.3650767. Epub 2024 May 11.
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.
MindScape旨在研究将时间序列行为模式(如对话参与度、睡眠、位置)与大语言模型(LLMs)相结合的益处,以创建一种新的情境化人工智能日志形式,促进自我反思和幸福感。我们认为,将行为感知整合到大型语言模型中可能会开创人工智能的新前沿。在这篇最新研究论文中,我们讨论了MindScape情境日志应用程序的设计,该应用程序使用大型语言模型和行为感知来生成情境化和个性化的日志提示,旨在鼓励自我反思和情感发展。我们还讨论了基于初步用户研究的MindScape大学生研究,以及我们即将开展的研究,以评估情境化人工智能日志在促进大学校园更好的幸福感方面的有效性。MindScape代表了一种在人工智能中嵌入行为智能的新应用类别。