What does the literature say about using robots on children with disabilities?
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to examine the extent and type of robots used for the rehabilitation and education of children and young people with CP and ASD and the associated outcomes. METHODS: The scholarly literature was systematically searched and analyzed. Articles were included if they reported the results of robots used or intended to be used for the rehabilitation and education of children and young people with CP and ASD during play and educative and social interaction activities. RESULTS: We found 15 robotic systems reported in 34 studies that provided a low level of evidence. The outcomes were mainly for children with ASD interaction and who had a reduction in autistic behaviour, and for CP cognitive development, learning, and play. CONCLUSION: More research is needed in this area using designs that provide higher validity. A centred design approach is needed for developing new low-cost robots for this population. Implications for rehabilitation In spite of the potential of robots to promote development in children with ASD and CP, the limited available evidence requires researchers to conduct studies with higher validity. The low level of evidence plus the need for specialized technical support should be considered critical factors before making the decision to purchase robots for use in treatment for children with CP and ASD. A user-entered design approach would increase the chances of success for robots to improve functional, learning, and educative outcomes in children with ASD and CP. We recommend that developers use this approach. The participation of interdisciplinary teams in the design, development, and implementation of new robotic systems is of extra value. We recommend the design and development of low-cost robotic systems to make robots more affordable.
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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 | 0.013 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".