Assistive Technologies for Internet Navigation: A Review of Screen Reader Solutions for the Blind and Visually Impaired
Bibliographic record
Abstract
The internet is a critical resource for individuals with visual impairments (VI), including those with low vision or blindness, enabling access to information and fostering independent participation in society. However, despite advancements in screen reader software, challenges persist in navigating complex and dynamic web content. This narrative review evaluates Assistive Technologies (AT), which enhance internet navigation for VI screen reader users, focusing on design strategies and usability outcomes. A systematic search across the ACM Digital Library, IEEE Xplore, JSTOR, and ScienceDirect identified 698 studies, of which 33 met the inclusion criteria, encompassing 502 participants. Key themes were extracted from bibliographic data, technology descriptions, evaluation methods, and outcomes. The findings reveal that most software-based technologies, including browser extensions and web applications, have limited hardware integration. These solutions improve navigation by providing alternative content representations, enhancing page structure understanding, and automating user tasks. Evaluations demonstrated increased task completion rates, reduced cognitive load, and enhanced efficiency, though variations in study designs and small sample sizes limit the generalizability of results. Despite their promise, these technologies face barriers, including a lack of commercial availability and rapid obsolescence. Addressing these challenges requires modular, user-centred designs employing intuitive multimodal interactions and robust information models tailored to navigation tasks. This review emphasizes the need for scalable, sustainable, and accessible AT innovations through open, collaborative development, ensuring equitable internet access for VI users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".