From tabbing to talking: LLM-driven conversational agents for web form interaction

Abstract

Web forms are a critical gateway to online services, yet they remain challenging for blind and visually impaired (BVI) users. Despite accessibility standards, screen reader workflows often prove frustrating and error-prone. This paper investigates how conversational agents (CAs) powered by Large Language Models (LLMs) can enable more accessible, voice-based interaction with online forms. Through a human-centered process involving guidelines analysis, stakeholder consultations, prototyping and a study with 18 BVI participants, we explored how LLM-driven CAs can support data entry, navigation, and input verification. Findings show that participants valued the system’s intuitiveness and reliability but highlighted tensions around verbosity, pacing, and control. From these insights, we derive design patterns that translate abstract voice interaction guidelines into concrete strategies for conversational form filling, contributing to more inclusive digital services for BVI users and beyond.

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