Online communities have become key platforms where young adults, actively seek and share information, including health knowledge. However, these users often face challenges when browsing these communities, such as fragmented content, varying information quality and unfamiliar terminology. Based on a survey with 56 participants and follow-up interviews, we identify common challenges and expected features for learning health knowledge. In this paper, we develop a computational workflow that integrates community content into a conversational agent named CanAnswer to facilitate health knowledge acquisition. Using colorectal cancer as a case study, we evaluate CanAnswer through a lab study with 24 participants and interviews with six medical experts. Results show that CanAnswer improves the recalled gained knowledge and reduces the task workload of the learning session. Our expert interviews (N=6) further confirm the reliability and usefulness of CanAnswer. We discuss the generality of CanAnswer and provide design considerations for enhancing the usefulness and credibility of community-powered learning tools.

翻译：在线社区已成为年轻人主动寻求和分享信息（包括健康知识）的关键平台。然而，这些用户在浏览社区时常常面临内容碎片化、信息质量参差不齐以及专业术语陌生等挑战。基于对56名参与者的调查及后续访谈，我们识别了学习健康知识过程中的常见困难与期望功能。本文开发了一种计算工作流，将社区内容整合至名为CanAnswer的对话智能体中，以促进健康知识获取。以结直肠癌为案例，我们通过包含24名参与者的实验室研究及六位医学专家的访谈对CanAnswer进行评估。结果表明，CanAnswer提升了知识记忆获取效果，并降低了学习任务的工作负荷。专家访谈（N=6）进一步证实了CanAnswer的可靠性与实用性。我们探讨了CanAnswer的普适性，并为增强社区驱动学习工具的有用性与可信度提供了设计考量。