From Search Agents to Dissemination Interfaces: Understanding Human Trust in Health Information from Conversational Search

📅 2026-08-21
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🤖 AI Summary
研究通过混合方法探讨了不同搜索代理和传播界面在健康信息中的信任问题,发现LLM驱动的对话搜索及精心设计的界面能显著影响用户信任。
📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

Human Trust
Health Information
Conversational Search
Dissemination Interfaces
Large Language Models
Innovation

Methods, ideas, or system contributions that make the work stand out.

Large Language Models
Conversational User Interfaces
Health Information-Seeking
Trust Perceptions
Dissemination Interfaces