探索社区驱动的对话代理用于健康知识获取：结直肠癌案例研究
Exploring Community-Powered Conversational Agent for Health Knowledge Acquisition: A Case Study in Colorectal Cancer
AI总结：
本文提出CanAnswer对话代理，通过整合社区内容提升结直肠癌健康知识获取效率，验证其在学习任务中的有效性与实用性。
摘要：
在线社区已成为年轻人主动寻求和分享信息，包括健康知识的重要平台。然而，这些用户在浏览这些社区时常常面临碎片化内容、信息质量参差不齐和不熟悉的术语等挑战。基于对56名参与者进行的调查和后续访谈，我们识别出学习健康知识时的常见挑战和预期功能。在本文中，我们开发了一种计算流程，将社区内容整合到名为CanAnswer的对话代理中，以促进健康知识的获取。以结直肠癌为案例研究，我们通过24名参与者的实验室研究和六位医学专家的访谈来评估CanAnswer。结果表明，CanAnswer提高了回忆获得的知识，并减少了学习会话的任务负荷。我们的专家访谈（N=6）进一步确认了CanAnswer的可靠性和实用性。我们讨论了CanAnswer的通用性，并提供了增强社区驱动学习工具实用性和可信度的设计考虑。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
