State of the Research on Artificial Intelligence Based Apps for Post-Secondary Students with Disabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We conducted a general Google search and a scoping review of various types of artificial intelligence (AI) based technology – mobile, web-based, software, hardware – used by college and university students to do schoolwork. The main findings indicate that (1) there is no generally agreed upon definition of AI, and (2) there is a huge discrepancy between the popular press articles that are behind the AI hype and the scientific literature. The popular press provides an overview of the AI tools available to students with disabilities and discusses how students can use these tools. The scientific literature is primarily devoted to tool development and has poor methodology. We conclude that the potential of AI for post-secondary students with disabilities is enormous, but that informed research about these tools is scant, with a profound lack of demonstrated scalability. Research needs to address “real-world” uses of AI-based tools by post-secondary students with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it