Kaspar in the wild: Experiences from deploying a small humanoid robot in a nursery school for children with autism
Bibliographic record
Abstract
Abstract This article describes a long-term study evaluating the use of the humanoid robot Kaspar in a specialist nursery for children with autism. The robot was used as a tool in the hands of teachers or volunteers, in the absence of the research team on-site. On average each child spent 16.53 months in the study. Staff and volunteers at the nursery were trained in using Kaspar and were using it in their day-to-day activities in the nursery. Our study combines an “in the wild” approach with a rigorous approach of collecting and including users’ feedback during an iterative evaluation and design cycle of the robot. This article focuses on the design of the study and the results from several interviews with the robot’s users. We also show results from the children’s developmental assessments by the teachers prior to and after the study. Results suggest a marked beneficial effect for the children from interacting with Kaspar. We highlight the challenges of transferring experimental technologies like Kaspar from a research setting into everyday practice in general and making it part of the day-to-day running of a nursery school in particular. Feedback from users led subsequently to many changes being made to Kaspar’s hardware and software. This type of invaluable feedback can only be gained in such long-term field studies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".