Co-Designing Digital Assistive Technologies for Autism Spectrum Disorder (ASD) Using Qualitative Approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study conducted a critical review to analyse qualitative studies related to the research, design, and implementation of digital assistive technologies for ASD, by evaluating their features in relation to conducting a co-design study for learners with ASD. This study identified 23 approaches used to study, design, configure, or develop digital assistive technologies for learners with ASD with studies focusing mostly on children, preschool, and adolescents. Qualitative approaches for co-design enabled collaboration from a wider community and the use of a multi-disciplinary approach; active involvement of learners with a user-centred approach; and the use of iterative or incremental design and development. Limitations and challenges revolved around restricted engagement to high-functioning learners; limited generalisability; implementation barriers in the real-world setting; lack of long-term evaluation or plan to assess effectiveness; and various implementation barriers. To engage people with moderate to severe ASD in co-design, researchers should scaffold their end-to-end design process using participatory design frameworks; embed various qualitative approaches within an iterative design, development, and testing process; and leverage tools that would enable structured customisation and personalisation of approach for participants.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it