Using the humanoid robot Kaspar in a Greek school environment to support children with Autism Spectrum Condition
Bibliographic record
Abstract
Abstract Previous studies conducted with the humanoid robot Kaspar in the UK have yielded many encouraging results. This paper examines the influence of conducting play sessions with Kaspar on the social and communication skills of children diagnosed with Autism Spectrum Condition (ASC) and suggests possible ways for using the robot as a (therapeutic) tool in a Greek school for children with special needs. Over a period of 10 weeks 7 children took part in a total of 111 individual play sessions with the Kaspar robot. Each child participated in between 12 and 18 sessions with the robot. The results from this study indicate that the play sessions with Kaspar appear to have positively influenced the behaviours of some of the children in specific domains such as communication and interaction, prompted speech, unprompted imitation and focus/attention. Furthermore, the children’s teachers expressed positive views regarding the impact of the play sessions on the children and offered interesting suggestions about the ways in which the robot could potentially be used in everyday teaching tasks and were eager to obtain a Kaspar for their classroom activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".