Understanding Family Needs: Informing Social Robot Design to Support Children with Disabilities to Engage in Play
Bibliographic record
Abstract
While children with disabilities often face barriers to play including reduced time, exclusion, and ill-suited toys, impacting their development, social robots provide the potential to help: they can motivate children, increase task engagement, and facilitate social interactions. However, social robots (and technological interventions in general) struggle to be adopted into regular use within homes by families, commonly being abandoned after a short time. Rather than focusing on the utility of these interventions, we instead look how they integrate into family needs and lifestyles. We designed and conducted a study where we engaged children living with disabilities and their families, using interactions with real robots and exploratory exercises, to learn about their perspectives, needs, and concerns regarding adopting a social companion robot in their home. We analyzed participant task engagement and feedback from the perspective of supporting play for children with disabilities and presented resulting design recommendations for addressing primary concerns and matching key expectations, and to support adoption pathways to improve the chances of success.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".