Towards Better Health Conversations: The Benefits of Context-seeking

TL;DR

This study explores how proactive context-seeking in conversational AI improves health-related interactions, showing that tailored, context-aware responses enhance user satisfaction and relevance in health information exchange.

Contribution

The paper introduces a novel 'Wayfinding AI' that proactively seeks context, demonstrating improved user perceptions over baseline models in health conversations.

Findings

Participants preferred context-seeking AI for health questions.

Proactive context solicitation increased perceived helpfulness and relevance.

Context-aware AI improved conversational engagement.

Abstract

Navigating health questions can be daunting in the modern information landscape. Large language models (LLMs) may provide tailored, accessible information, but also risk being inaccurate, biased or misleading. We present insights from 4 mixed-methods studies (total N=163), examining how people interact with LLMs for their own health questions. Qualitative studies revealed the importance of context-seeking in conversational AIs to elicit specific details a person may not volunteer or know to share. Context-seeking by LLMs was valued by participants, even if it meant deferring an answer for several turns. Incorporating these insights, we developed a "Wayfinding AI" to proactively solicit context. In a randomized, blinded study, participants rated the Wayfinding AI as more helpful, relevant, and tailored to their concerns compared to a baseline AI. These results demonstrate the strong impact of proactive context-seeking on conversational dynamics, and suggest design patterns for conversational AI to help navigate health topics.