mlsanityData to 14 Oct 2026

Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents

arXiv 2608.25382 ↗First posted 26 Aug 2026cs.HC cs.AI cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 124 references that are in the map. First posted on arXiv 26 Aug 2026.

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17 of 124 references.

References · 17

  1. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 87
  2. 2023Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfacescited by 72
  3. 2024Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Taskscited by 38
  4. 2016Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Accesscited by 36
  5. 2017Iris: A Conversational Agent for Complex Taskscited by 22
  6. 2024The Use of Generative Search Engines for Knowledge Work and Complex Taskscited by 18
  7. 2025"Before, I Asked My Mom, Now I Ask ChatGPT": Visual Privacy Management with Generative AI for Blind and Low-Vision Peoplecited by 6
  8. 2021Technology Developments in Touch-Based Accessible Graphics: A Systematic Review of Research 2010-2020cited by 4
  9. 2023TADA: Making Node-link Diagrams Accessible to Blind and Low-Vision Peoplecited by 4
  10. 2013DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Userscited by 3
  11. 2023Comparing Conventional and Conversational Search Interaction using Implicit Evaluation Methodscited by 3
  12. 2025Using Tactile Charts to Support Comprehension and Learning of Complex Visualizations for Blind and Low-Vision Individualscited by 2
5 more
  1. 2025Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learningcited by 2
  2. 2024Functional Flexibility in Generative AI Interfaces: Text Editing with LLMs through Conversations, Toolbars, and Promptscited by 1
  3. 2024Generative AI's aggregated knowledge versus web-based curated knowledgecited by 1
  4. 2026"If We Had the Information That We Need to Interpret the World Around Us, We Wouldn't Be Disabled:" Barriers and Opportunities in Information Work among Blind and Sighted Colleaguescited by 1
  5. 2026Characterization and Effects of CS2 Learning with GenAI, Visualization, and Human Supportcited by 1

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