Changing times: emerging technologies for students with disabilities in higher education
Bibliographic record
Abstract
In this chapter we explore a variety of topics related to emerging technologies in the post-secondary education of students with a range of disabilities. Much has changed in the past decade including: (1) the impact and evolution of the increasing accessibility of general use technologies, comprising built-in accessibility features and accessibility checkers and correctors in both desktop and mobile operating systems and apps; (2) the increasing use of artificial intelligence (AI) in mainstream technologies, including AI-based captioning and translation into various languages; and (3) developments in braille and sign language technologies, virtual reality, voice-based web searches, wearable technologies, indoor navigation, and the potential of robots in science classrooms. We comment on the accessibility - or lack thereof - of virtual and augmented reality and highlight barriers to students with disabilities such as inaccessibility of science-based technologies, limited numbers of individuals with disabilities involved in training AI-based technologies, and the continuing high cost of some essential assistive technologies. We note the need to recruit individuals with disabilities to assist with the development of products from their inception, to test usability of products already in development, and to participate as researchers. We emphasize that developers need to assess their products’ continuing accessibility and to be attentive to user feedback. We also stress the need for colleges and universities to continue to engage their stakeholders, such as publishers of academic material, procurement officers, campus IT specialists, teaching and learning specialists, instructional designers, and librarians to ensure that accessibility standards are met throughout the institution. Finally, we note concerns related to the new technologies about privacy, ethics, and product safety.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".