Bibliographic record
Abstract
The literature has established that if instructors shy away from implementing Universal Design for Learning (UDL) it is often because of lucid and tangible fears about workload. In parallel, the emergence of gen AI has immediately triggered hopes that large language models (LLMs) might be successfully used to support the challenging planning tasks of higher education instructors; it has immediately struck scholars that this might include supporting instructors who might have fears about workload and competencies in relation to UDL implementation in their classes. This study explored the degree to which an LLM could be effective in supporting an instructor redesign two Masters of Education courses in order to make them more aligned with UDL than they already were. The theoretical lens used in this project was the social model of disability. The methodological framework used was action research. The research team prompted the LLM for UDL strategies. A process of triangulation invited students having previously taken the courses to assess the effectiveness of the redesign. The findings suggest that gen AI can indeed support the UDL redesign of courses. Concerns are, however, raised because mastering the prompting competencies necessary may be as complex as the UDL redesign itself.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".