Ecological Design of an Augmentative and Alternative Communication Device Interface
Bibliographic record
Abstract
Autism spectrum disorder, cerebral palsy, brain stem stroke, and neurological injury are examples of conditions that may limit vocal communication. Augmentative and alternative communication (AAC) systems can provide a communication pathway to users who experience such complex communication needs, facilitating their societal participation and supporting some ability to direct their own care. We adapted the cognitive work analysis (CWA) framework to a linguistic domain for insights into an AAC design that best supports users’ communication. First, we applied the work domain analysis (WDA) to a popular commercial AAC system, Proloquo2Go. Data were gathered from guided AAC system use, domain experts, and the syntactic rules of the English language. The WDA exposed unmet needs in the commercial system. We then applied worker competency analysis to consider different approaches to present information and support user actions. The design included graphic forms and process views, and their integration into viewports and the workspace. Our novel application of CWA uncovered new considerations in AAC interface design and presents a nascent area of investigation, namely, AAC displays that more effectively support users’ goals. Future investigation will evaluate the mental workload of this AAC interface compared to that of current commercially available systems.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".