Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents
cs.HC, cs.AI, cs.IR
Submitted: 2026-08-26
Updated: 2026-08-26
Comments: 17 pages, 12 figures, accepted at ASSETS 2026
License: http://creativecommons.org/licenses/by/4.0/
The gist: Blind and low-vision (BLV) users are increasingly engaging with large language model (LLM) interfaces to access documents, but it is unclear how such systems support or hinder their ability to build
Terminology
Abstract
Blind and low-vision (BLV) users are increasingly engaging with large language model (LLM) interfaces to access documents, but it is unclear how such systems support or hinder their ability to build interconnected knowledge. To examine this gap, we compared a Question-Answer Interface (QAI) that supports open-ended conversational inquiry, with a Document Interface (DI) based mostly on traditional structured text document navigation. We recruited 16 BLV screen reader users where they used both interfaces to explore two fictional worlds. Data from interaction logs, concept maps, decision-based tasks, and semi-structured interviews provide comparative insights into how interface design supports knowledge construction. Findings show that participants visited more distinct documents with the DI and formed larger and more correct mental models with the DI than with the QAI. They were also more able to apply knowledge they had gained. Simultaneously, many still preferred the QAI and often estimated that they had explored more, formed better mental models and applied their models better when acquiring the information with the QAI, despite this not being the case. Our analysis suggests possible interface design reasons for these differences and highlights some of the risks introduced by using question-answer interfaces to access information spaces.
Sources
- Using Tactile Charts to Support Comprehension and Learning of Complex Visualizations for Blind and Low-Vision Individuals
- Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning
- Functional Flexibility in Generative AI Interfaces: Text Editing with LLMs through Conversations, Toolbars, and Prompts
- Generative AI's aggregated knowledge versus web-based curated knowledge
- Characterization and Effects of CS2 Learning with GenAI, Visualization, and Human Support
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