From Letterboards to Holograms: Advancing Assistive Technology for Nonspeaking Autistic Individuals with the HoloBoard
Bibliographic record
Abstract
About one-third of autistic individuals are nonspeaking, i.e., they cannot use speech to convey their thoughts reliably. Many in this population communicate via spelling, a process in which they point to letters on a letterboard held upright in their field of view by a trained Communication and Regulation Partner (CRP). This paper focuses on transitioning such individuals to more independent, digital spelling that requires less support from the CRP, a goal most nonspeakers we consulted with desire. To enable this transition, we followed an approach that mimics an environment familiar to the nonspeaker and that harnesses the skills they already possess from physical letterboard training. Using this approach, we developed HoloBoard, a system that allows a nonspeaker, their CRP, and others, e.g., researchers, to share a common Augmented Reality (AR) environment containing a virtual letterboard. We configured the system to offer a brief (less than 10 minutes, on average) training module with graduated spelling tasks on the virtual letterboard. In a study involving 23 participants, 16 completed the entire module. These participants were able to spell words on the virtual letterboard without the CRP holding that board, an outcome we had not expected. When offered the opportunity to continue interacting with the virtual letterboard after the training module, 14 performed more complicated tasks than we had anticipated, spelling full sentences, or even offering feedback on the HoloBoard using solely the virtual board. Furthermore, five of these participants used the system solo, i.e., with the CRP and researchers absent from the virtual environment. These results suggest that training with the HoloBoard can lay the foundation for more independent communication, providing new social and educational opportunities for this marginalized population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".