Co-Designing Digital Assistive Technologies for Autism Spectrum Disorder (ASD) Using Qualitative Approaches
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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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 | 0.125 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".