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This study critically evaluates how Large Language Model interfaces impact knowledge construction for blind and low-vision users, revealing that traditional document navigation yields more accurate mental models than conversational Q&A systems despite the latter's popularity. The findings highlight a significant risk of overestimating comprehension when relying on generative AI chatbots for complex information retrieval tasks.
Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents
arXiv:2608.25382v1 Announce Type: cross 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.
Title:Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents
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.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
arXiv:2608.25382 [cs.HC]
(or arXiv:2608.25382v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2608.25382 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3797867.3829036 Focus to learn more DOI(s) linking to related resources
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