Using Robot-Mediated Applied Drama to Foster Anti-Bullying Peer Support
Bibliographic record
Abstract
Applied drama refers to the use of theatrical practices in contexts such as education, therapy, or community-building. We present Robot-Mediated Applied Drama as a medium for safely exploring sensitive topics with children and propose to develop RE-Mind (short for Robots Empowering Minds): a pedagogical platform that aims to use role-playing with robots for fostering anti-bullying peer support among children. We borrow techniques from applied drama to build the human-robot interaction models used in this system. Specifically, we draw from Augusto Boal’s Forum Theatre: a theatrical exercise in which spectators of a drama are invited to become “spect-actors”. That is, they watch the performance twice; the second time, they stop the performance and change its direction and potential conclusion by suggesting different actions to the protagonist. This allows children to use the proposed system to first observe a bullying scenario between two robots, and then intervene by controlling a third robot that is a bystander to bullying, and by doing so practice their intervention strategies. Applied drama engages peers in situated learning and allows them to take reflective, participatory action. In this paper, we argue that using robots provides a buffer for participants to safely explore sensitive topics in a private setting. We present background literature to support using applied drama as an effective vehicle for learning and discuss related work on technology-based anti-bullying interventions. Finally, we define Robot-Mediated Applied Drama and discuss how social robots lend themselves well to this practice.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".