AI 中文总结
本研究通过两项混合方法研究，发现人们对ChatGPT提供的健康信息信任显著高于谷歌，且传播界面类型会显著影响这种信任，为开发可信的LLM驱动健康工具提供了关键见解。
AI 中文摘要
通过对话式用户界面（CUIs）部署的大语言模型（LLMs），与谷歌等传统搜索引擎相比，正通过提供即时、互动的体验改变人们获取健康信息的方式。然而，搜索智能体的类型和用于传播信息的界面如何影响信任，这一问题尚未得到充分探索。本研究整合了两项混合方法研究（实验室实验和访谈），以全面探索不同搜索智能体和传播界面下人们对健康信息的信任感知。在研究1（样本量N=21）中，我们调查了人们对来自ChatGPT和谷歌的健康信息的信任，涉及三类与健康相关的搜索任务。结果显示，人们对ChatGPT提供的健康信息的信任显著更高，凸显了LLM驱动的对话式搜索的潜力。在此基础上，研究2（样本量N=20）扩展了研究内容，通过比较三种界面（基于文本、基于语音和具身界面，均来自同一LLM），探索传播界面如何影响人们对LLM提供的健康信息的信任。研究结果显示，不同传播界面下的信任存在显著差异。两项研究的访谈揭示了影响人们对LLM驱动的对话式搜索信任的关键因素，包括来源可信度、参与者的搜索自主权、先前知识以及交互风格和模态。我们的研究结果凸显了LLM驱动的对话式搜索在改变健康信息获取方面的潜力，强调了可信搜索智能体与精心设计的传播界面在塑造信任方面的相互作用。这些见解对于开发有效、可信的LLM驱动健康工具、优化健康信息获取体验至关重要。
英文摘要
Large Language Models (LLMs) deployed through Conversational User Interfaces (CUIs) are transforming health information-seeking by offering immediate, interactive experiences compared to traditional search engines like Google. However, how trust is influenced by both the types of search agents and the interface used to disseminate the information remains underexplored. This research integrates two mixed-methods studies (lab sessions and interviews) to comprehensively explore trust perceptions in health information across different search agents and dissemination interfaces. In Study 1 (N=21), we investigated trust in health information sourced from ChatGPT and Google across three types of health-related search tasks. Results showed significantly higher trust in health information from ChatGPT, highlighting the promise of LLM-powered conversational search. Building on this, Study 2 (N=20) extended the investigation to explore how the dissemination interface influences trust in LLM-sourced health information by comparing three interfaces: text-based, speech-based, and embodied, all sourcing from the same LLM. Findings revealed significant trust variations across the dissemination interfaces. Interviews from both studies revealed key factors influencing trust in LLM-powered conversational search, including source credibility, participants' search autonomy, and prior knowledge as well as the interaction style and modality. Our findings highlight the potential of LLM-powered conversational search to transform health information-seeking, underscoring the interplay between the credible search agents and the thoughtfully designed dissemination interfaces in shaping trust. These insights are crucial for developing effective, trustworthy LLM-powered health tools to enhance the health information-seeking experience.
