“There Are Things I Can’t Ask People”: User Perceptions of LLM-Based Chatbot for Sexual and Reproductive Health in India
TL;DR
The findings reveal three interconnected themes: first, LLMs serve as low-stakes knowledge companions that reduce the cognitive and social burden of SRH information-seeking; secondly, privacy needs are relational and situational rather than uniform, shaped by domestic contexts and family structures; and lastly, LLM responses exhibit systematic gender bias in SRH framing—a pattern which carries significant implications for equitable chatbot design.
Abstract
The rapid growth of large language models (LLMs) is reshaping access to health information, yet their role in sexual and reproductive health (SRH)—a highly stigmatized and sensitive domain is a nascent area of research, particularly in the Indian context. This study investigates how individuals perceive and engage with an LLM-based chatbot for SRH, examining trust boundaries, privacy expectations, and the cultural dimensions of LLM-mediated health communication. We designed a Retrieval-Augmented Generation (RAG) chatbot for sexual health and conducted a user study in which fifteen participants interacted with the system, followed by semi-structured interviews. Our findings reveal three interconnected themes: First, LLMs serve as low-stakes knowledge companions that reduce the cognitive and social burden of SRH information-seeking; secondly, privacy needs are relational and situational rather than uniform, shaped by domestic contexts and family structures; and lastly, LLM responses exhibit systematic gender bias in SRH framing—a pattern which carries significant implications for equitable chatbot design. We contribute design recommendations for culturally sensitive, privacy-aware, and gender-equitable LLM systems for SRH communication.