AAC with Automated Vocabulary from Photographs: Insights from School and Speech-Language Therapy Settings
Bibliographic record
Abstract
Traditional symbol-based AAC devices impose meta-linguistic and memory demands on individuals with complex communication needs and hinder conversation partners from stimulating symbolic language in meaningful moments. This work presents a prototype application that generates situation-specific communication boards formed by a combination of descriptive, narrative, and semantic related words and phrases inferred automatically from photographs. Through semi-structured interviews with AAC professionals, we investigate how this prototype was used to support communication and language learning in naturalistic school and therapy settings. We find that the immediacy of vocabulary reduces conversation partners’ workload, opens up opportunities for AAC stimulation, and facilitates symbolic understanding and sentence construction. We contribute a nuanced understanding of how vocabularies generated automatically from photographs can support individuals with complex communication needs in using and learning symbolic AAC, offering insights into the design of automatic vocabulary generation methods and interfaces to better support various scenarios of use and goals.
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How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".