Teaching social robots: the effect of robot mistakes on children's learning-through-teaching
Bibliographic record
Abstract
Social robots are increasingly being designed for use in educational contexts, including in the role of a tutee. However, little is known about how robot behavior affects children's learning-through-teaching. We examined whether the frequency and type of robot mistakes affected children's teaching behaviors (basic and advanced), and subsequent learning, when teaching a social robot. Eight to 11-year-olds ( N = 114) taught a novel classification scheme to a humanoid robot. Children taught a robot that either made no mistakes, typical mistakes (errors on untaught material; accuracy on previously taught material), or atypical mistakes (errors on previously taught material; accuracy on untaught material). Following teaching, children's knowledge of the classification scheme was assessed, and they evaluated their own teaching and both their own and the robot's learning. Children generated more teaching strategies when working with one of the robots that made mistakes. While children indicated that the robot that made typical mistakes learned better than the one that made atypical mistakes, children themselves demonstrated the most learning gains if they taught the robot that made atypical mistakes. Children who demonstrated more teaching behaviors showed better learning, but teaching behaviors did not account for the learning advantage of working with the atypical mistake robot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".