Exploring Community-Powered Conversational Agent for Health Knowledge Acquisition: A Case Study in Colorectal Cancer

TL;DR

This paper presents CanAnswer, a community-powered conversational agent designed to improve health knowledge acquisition from online communities, demonstrated through a colorectal cancer case study with positive results in knowledge recall and workload reduction.

Contribution

It introduces a novel computational workflow that integrates community content into a conversational agent for health education, validated through empirical evaluation.

Findings

CanAnswer enhances knowledge recall in users.

It reduces the workload during health learning sessions.

Expert feedback confirms the tool's reliability and usefulness.

Abstract

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.