Global trends in Braille literacy and assistive technologies: A bibliometric analysis (1985–2024)
Bibliographic record
Abstract
This study investigates global research trends in Braille literacy and assistive technologies from 1985 to 2024, highlighting persistent challenges such as limited access to cost-effective solutions, disparities in research participation, and inadequate teaching resources for visually impaired learners. Despite advancements in technology, barriers including high costs and lack of infrastructure hinder equitable implementation, particularly in low-resource settings. Using bibliometric analysis of 225 publications from Scopus, this research identifies trends, geographic contributions, and thematic advancements. This study is a scientific paper that presents an analysis of research on strategies for improving reading and writing among the blind, and results show that research in this area is increasing, with the highest contribution coming from the United States, Canada and Malaysia. Emerging trends such as “e-learning” and “3D printing” indicate that technology is playing a significant role in providing alternative strategies for teaching Braille reading and writing, thus, this study has the potential to bring about a paradigm shift in teaching and learning among the blind. This study is significant as it not only identifies key research findings but also recommends the use of low-cost materials to develop motor skills and tactile sensitivity, and this can ensure a wider access to individuals from different socioeconomic backgrounds. Furthermore, it also recommends the development of pedagogical books to facilitate initial reading and writing of Braille, because this study is based on the United Nations Sustainable Development Goal 4, which advocates for ensuring that all individuals have equal access to quality education. The study recommends collaboration and ideas which can be scaled up; therefore, this is necessary to ensure the effective learning and reading of the blind, so it is essential to implement these recommendations to achieve the desired outcomes.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.093 | 0.157 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".