生成型人工智能响应是一笔一打;让它们算是挑战 - - 评估医疗保健聊天机器人的信息呈现风格,使用层次化的贝叶斯回归模型
Samuel N Koscelny1, Sara Sadralashrafi1, David M Neyens2
1Department of Industrial Engineering, 100 Freeman Hall, Clemson University, Clemson, SC, 29634, USA.
Applied ergonomics
|April 18, 2025
概括
医疗保健聊天机器人中的大型语言模型可以改善信息传递. 然而,对话风格可能会提高可用性,但会降低信任,影响用户体验.
科学领域:
- 人与计算机的交互
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 为通过基于Web的聊天机器人传播健康信息提供了新的途径.
- 由于健康信息的复杂性和技术性质,有效的人类-人工智能交互设计至关重要.
研究的目的:
- 调查医疗聊天机器人的不同沟通和语言风格如何影响用户对有效性,可信度和可用性的看法.
- 确定由大型语言模型驱动的医疗保健聊天机器人的最佳设计策略.
主要方法:
- 采用2×2对象之间的实验设计,操纵沟通风格 (对话式与信息式) 和语言风格 (技术性与非技术性).
- 使用层级贝叶斯回归模型来分析这些变量对用户体验指标的影响.
主要成果:
- 低感知可用性显著降低了医疗保健聊天机器人的整体有效性.
- 一种对话式的沟通方式增加了感知到的可用性,但同时减少了用户对聊天机器人的信任.
结论:
- 信息呈现风格显著影响医疗保健聊天机器人的用户体验.
- 设计人员必须平衡对话元素的可用性和清晰度,以保持用户对AI健康信息系统的信任.
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