Most academic research is ultimately disseminated through documents in the PDF format. This format has advantages in flexibility and portability, but presents challenges for accessibility that have stubbornly resisted solutions despite decades of attempts. Tagging PDFs is hard to automate because tags are currently generated visually, not semantically, which makes the output cluttered and manual correction tedious and error-prone. Ironically, this semantic structure already exists during authoring but is discarded during PDF rendering. This raises an obvious question, can we use this lost semantic information to better automate tagging in PDFs? In this paper, we develop iTagPDF, a system that refines generated metadata using the semantics in the source documents of research papers. We demonstrate that the metadata generated by iTagPDF already surpasses what authors currently submit to ACM conferences on many criteria. Our approach represents a concrete step toward finally automating accessibility remediation in research paper PDFs.
Research touts universal participation through accessibility initiatives, yet blind and low-vision (BLV) researchers face systematic exclusion as visual representations dominate modern research workflows. To materialize inclusive processes, we, as BLV researchers, examined how our peers combat inaccessible infrastructures. Through an explanatory sequential mixed-methods approach, we conducted a cross-sectional, observational survey (n=57) and follow-up semi-structured interviews (n=15), analyzing open-ended data using reflexive thematic analysis and framing findings through activity theory to highlight research's systemic shortcomings. We expose how BLV researchers sacrifice autonomy and shoulder physical burdens, with nearly one-fifth unable to independently perform literature review or evaluate visual outputs, delegating tasks to sighted colleagues or relying on AI-driven retrieval to circumvent fatigue. Researchers also voiced frustration with specialized tools, citing developers' performative responses and losing deserved professional accolades. We seek follow-through on research's promises through design recommendations that reconceptualize accessibility as fundamental to successful research and supporting BLV scholars' workflows.
As social virtual reality (VR) grows more popular, addressing accessibility for blind and low vision (BLV) users is increasingly critical. Researchers have proposed an AI “sighted guide” to help users navigate VR and answer their questions, but it has not been studied with users. To address this gap, we developed a large language model (LLM)-powered guide and studied its use with 16 BLV participants in virtual environments with confederates posing as other users. We found that when alone, participants treated the guide as a tool, but treated it companionably around others, giving it nicknames, rationalizing its mistakes with its appearance, and encouraging confederate-guide interaction. Our work furthers understanding of guides as a versatile method for VR accessibility and presents design recommendations for future guides.
Independent and secret voting is a constitutional right, yet blind and low-vision voters (BLVs) continue to face barriers when casting their votes. Existing methods such as tactile templates often require braille literacy or assistance, while electronic ballot-marking devices raise cost and security concerns. We present I-VAMOS, a voting assistance system that enables BLVs to cast paper ballots securely and independently. Based on participatory sessions with BLVs, I-VAMOS integrates a ballot slide frame, a spring-loaded stamp, and real-time OCR-based speech and visual feedback, operating offline without the need for customized templates. With the improved I-VAMOS, we conducted a user study (n=16), balanced across vision status, braille literacy, and age. Results showed that I-VAMOS significantly reduced workload (NASA–TLX; 26.1) and improved stamping accuracy (91.7%) and usability (SUS; 79.1) compared to existing aids, though took longer completion times (29.6s). These findings emphasize that I-VAMOS enables independent and confidential voting for BLVs.
Generative AI (GenAI) is evolving from standalone tools to interconnected ecosystems that integrate chatbots, cloud platforms, and third-party services. While this ecosystem model enables personalization and extended services, it also introduces complex information flows and amplifies privacy risks. Existing solutions focus on system-level protections, offering little support for users to make meaningful privacy choices. To address this gap, we conducted two vignette-based survey studies with 486 participants and a follow-up interview study with 16 participants. We also explored users’ needs and preferences for privacy choice design across both GenAI personalization and data-sharing. Our results reveal paradoxical patterns: participants sometimes trusted third-party ecosystems more for personalization but perceived greater control in first-party ecosystems when data was shared externally. We discuss design implications for privacy choice interfaces that enhance transparency, control, and trust in GenAI ecosystems.
Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats—tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.