Assistive Technology in K-12 Schools on Prince Edward Island:
Bibliographic record
Abstract
To understand the current state of assistive technology (AT) use in Prince Edward Island’s K–12 schools, we conducted a landscape analysis to identify key trends, opportunities, and gaps. Our research involved an online survey distributed to educators across the island, capturing their perceptions, experiences, and usage patterns of assistive technology in classroom settings. Findings suggest that while educators are committed professionals striving to support diverse learners, they face significant barriers in effectively implementing AT. These barriers include limited access to professional learning, student stigma, and challenges in maintaining and updating AT tools. Despite a shared commitment to inclusive education, many educators report feeling underprepared to integrate assistive technologies in ways that fully support student learning needs. This study underscores the urgent need for a coordinated, system-wide approach to AT implementation. This includes robust policy development, ongoing educator training, dedicated technical support, and sustainable funding models for acquiring and maintaining technology. Without these foundational supports, the potential of assistive technology to enhance educational access and outcomes for all students remains unrealized. Our findings point to clear pathways for strengthening AT use across the province, ensuring equitable learning opportunities for students with diverse needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".