设计一个随时适应性干预,具有触发器检测和生成聊天机器人:戒烟使用案例
Kyana Bosschaerts1, Arian Kashefi2, Lieven De Marez2
1IDLab, Ghent University-imec, Gent, Belgium.
Digital health
|October 21, 2025
概括
这项研究介绍了一种用于使用大型语言模型 (LLM) 来个性化行为改变支持的即时适应性干预 (JITAI) 的新型架构. 该系统有效地检测触发因素,并提供量身定制的私人干预措施,如在戒烟中所示.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 人工智能的人工智能
背景情况:
- 准时适应性干预 (JITAI) 在有效地定制信息和定时干预方面面临挑战.
- 个性化的支持对于成功的行为改变至关重要.
研究的目的:
- 引入JITAI的架构,将信息定制和及时干预交付集成在一起.
- 利用大型语言模型 (LLM) 来提供个性化的行为变化支持.
主要方法:
- 开发了一种系统,将触发器检测结合起来,以获得最佳的干预时间,并在LLM上进行快速工程.
- 应用该系统作为戒烟的概念证明.
- 通过专家采访评估聊天机器人的反应,以确定其正确性,相关性和个性化.
主要成果:
- 一位专家证实,聊天机器人的支持是正确的,个性化的,并根据用户触发和情况量身定制.
- 聊天机器人的功能被认为是有效和可接受的,以支持戒烟.
- 提供了一些微小的建议,以进一步提高.
结论:
- 具有快速工程的大型语言模型 (LLM) 可以为JITAI创建个性化,触发响应的聊天机器人.
- 将触发器检测和生成聊天机器人集成到JITAI中是可行的,同时保持用户隐私.
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