情境人工智能日志:整合 LLM 和时间序列行为传感技术,利用 MindScape 应用程序促进自我反思和幸福感。

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
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引用次数: 0

摘要

MindScape 旨在研究将时间序列行为模式(如对话参与、睡眠、位置)与大型语言模型(LLMs)相结合的益处,以创建一种新形式的情境人工智能日志,促进自我反思和身心健康。我们认为,将行为感知集成到大型语言模型中很可能会开辟人工智能的新领域。在这篇 "晚期突破性工作 "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 LLM 和行为感应生成情境和个性化日志提示,以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,以评估情境式人工智能日志在促进大学校园健康方面的有效性。MindScape 代表了一种将行为智能嵌入人工智能的新应用类别。
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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.

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