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语境人工智能日记:整合LLM和时间序列行为传感技术以使用MindScape应用程序促进自我反射和幸福

Subigya Nepal¹, Arvind Pillai¹, William Campbell²

¹Dartmouth College, Hanover, New Hampshire, USA.

Extended abstracts on Human factors in computing systems. CHI Conference

|July 29, 2024

概括

MindScape 将行为感知与大型语言模型 (LLM) 集成,用于上下文AI日记. 这种方法旨在通过基于用户行为模式提供个性化的提示来增强自我反思和幸福感.

科学领域:

人工智能的人工智能
人与计算机的交互
数字福利数字福利

背景情况:

目前的日记方法缺乏个性化和上下文.
整合行为数据可以提供更深入的自我洞察力.
大型语言模型 (LLM) 显示了先进AI应用的潜力.

研究的目的:

探索将时间序列行为模式与人工智能日记的LLM相结合的好处.
介绍MindScape应用程序,这是一个新的语境AI日记工具.
评估人工智能日记在促进大学生福祉方面的有效性.

主要方法:

开发MindScape应用程序,整合LLM和行为传感 (对话参与,睡眠,位置).
设计个性化的日记可以促进自我反思和情感发展.
与大学生进行用户研究,以评估应用程序的影响.

主要成果:

预先的用户研究表明了增强自我反思的潜力.
即将进行的研究将对幸福感的改善进行定量评估.
MindScape展示了一类新的AI应用程序,其中嵌入了行为智能.

结论:

将行为传感与LLM集成,在人工智能领域为个性化幸福提供了一个新的前沿.
语境AI日记显示了改善自我反思和情感发展的前景.
MindScape代表了迈向人工智能驱动的心理健康支持的重要一步.

关键词:

在这里,我们可以看到AIAIAI.行为传感器是一种行为传感器.在日记上写日记.大型语言模型心理健康心理健康被动感应是一种被动感应.自我反思自我反思智能手机是一个智能手机.幸福是一种幸福.

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