Improving Survey Participation in Low-Literacy Populations Through Value-Sensitive Conversational AI

Abstract

Collecting reliable social data from low-literacy populations remains a persistent challenge, particularly when surveys involve sensitive topics and marginalized communities. Traditional paper-based and web-based survey modalities often suffer from high attrition and incomplete responses due to literacy barriers, social pressure, and interactional discomfort. In this paper, we present findings from an initial field evaluation comparing multiple survey modalities—paper-based interviews, digital web-based surveys, conversational AI surveys, and conversational AI enhanced with layered value-sensitive design—conducted with low-literacy women across India. Using data from 315 participants, we show that conversational AI significantly improves survey completion rates relative to traditional modalities, with the highest completion and lowest drop-off observed when value-sensitive and culturally aligned conversational design elements are fully integrated. These results demonstrate the importance of human-centered and value-sensitive interaction design in enabling inclusive, ethical, and scalable data collection for AI-for-social-good applications.