Special Education Teachers' Perceptions of Using Artificial Intelligence in Educating Students with Disabilities
Bibliographic record
Abstract
Background: Artificial intelligence technologies improve the learning environment; in the near future, they are expected to provide great benefits for students and teachers, in general, and for those with disabilities and their teachers, in particular. Objective: This research has aimed at identifying the perceptions of special education teachers about the use of artificial intelligence in teaching students with disabilities as well as identifying the impact of some variables, such as the number of years of experience, disability category, or the school stage, on these perceptions. Methods and Participants: The research was based on the descriptive approach. The research sample consists of 301 male and female teachers of students with disabilities from Riyadh, Kingdom of Saudi Arabia. It includes 138 males and 163 females, divided into a group of special education programs. The research used a questionnaire on the perceptions of special education teachers about the use of artificial intelligence in educating students with disabilities. Results: The research findings showed that these teachers' perceptions were mostly neutral, that there are differences in their perceptions due to the number of years of experience, and that there are no differences in their perceptions due to the disability category or school stage variable. Conclusions: As artificial intelligence is considered one of the modern variables in the field of education for people with disabilities in the Arab environment, it is expected to support personal education, assistive technologies, data-based decision-making when teaching people with disabilities, and promoting inclusion. The research also presented a questionnaire identifying special education teachers' perceptions of artificial intelligence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".