Dual-Source Retrieval-Augmented Generation Chatbot for Women’s Health (HerCare): Design and Multimethod Evaluation Study
Abstract
Abstract Background Conversational agents for women’s health often fail to meet user needs, offering either clinically sterile advice or unreliable peer anecdotes. This limitation creates a tension between the need for factual safety and emotional resonance in sensitive health contexts. Objective We aimed to address this gap by developing and conducting a formative evaluation of HerCare, a conversational agent built on a novel dual-source retrieval-augmented generation architecture. The system integrates expert medical knowledge with peer narratives and makes the provenance of each response visible to users, enabling trust calibration through transparent source attribution. Methods We conducted a remote, web-based single-session field study (December 2024 to January 2025; North Dakota State University Institutional Review Board Protocol #IRB0005368) with 243 completers (from 335 eligible, consenting visitors) recruited via social media (Facebook [Meta], Reddit, and Instagram [Meta]) and university mailing lists. Eligible participants self-identified as women aged 18‐45 years with English proficiency and internet access. We used a quantitative multimethod evaluation, combining standardized self-report metrics—the Chatbot Usability Questionnaire and net promoter score (NPS)—with computational linguistic analyses (VADER [Valence Aware Dictionary and Sentiment Reasoner] sentiment analysis and NRC [National Research Council] Emotion Lexicon) of 1191 conversational turns. Results…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.