面向个性化的说服力社会机器人,以改变医疗保健中的行为:一个概念框架
Elie Maalouly1, Alessandra Rossi2, Silvia Rossi2
1Department of Neuroscience and Reproductive and Odontostomatological Sciences, University of Naples Federico II, Naples, Italy.
Frontiers in robotics and AI
|March 12, 2026
概括
本研究介绍了个性化对话代理的框架,以帮助改变行为. 它整合了用户数据和有说服力的语言,以实现更有效的医疗干预.
科学领域:
- 人与计算机的交互
- 行为科学 行为科学
- 医疗保健中的人工智能
背景情况:
- 改变行为干预措施往往缺乏个性化,这限制了它们的有效性.
- 行为变化的理论模型为影响个人行为提供了洞察力.
- 交谈代理提供了一个可扩展的平台来提供干预措施.
研究的目的:
- 提出一个概念框架来设计个性化的说服性对话代理.
- 整合行为模型,用户个性化和说服性语言生成以改变行为.
- 通过量身定制的对话,提高医疗保健干预措施的有效性.
主要方法:
- 利用行为变化的关键理论模型.
- 在基于对话的干预中探索个性化的作用.
- 检查有说服力的语言生成技术.
主要成果:
- 提出了一个分层的框架,连接行为模型,个性化和语言生成.
- 个性化对于调整干预措施以适应个体特征和环境至关重要.
- 说服性语言可以增强参与和影响行为改变策略.
结论:
- 拟议的框架为开发有效的个性化对话代理提供了一个结构化的方法.
- 将行为科学原理与人工智能相结合,是成功数字健康干预的关键.
- 未来的研究应该专注于在社会机器人干预中实施和评估这一框架.
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