Self-Talk with Superhero Zip: Supporting Children’s Socioemotional Learning with Conversational Agents
Bibliographic record
Abstract
Socioemotional competencies are fundamental for children’s growth and success, and prior work shows that in some instances, technology can support children in acquiring these skills. Here, we examine whether children can learn to use a socioemotional strategy known as “self-talk” from a conversational agent (CA). To investigate this question, we designed and built “Self-Talk with Superhero Zip,” an interactive CA experience, and deployed it for one week in ten family homes to pairs of siblings between the ages of five and ten (N = 20). We found that children could recall and accurately describe the lessons taught by the intervention, and we saw indications of children applying self-talk in daily life. Targeting sibling pairs rather than individual users proved to be a design challenge in its own right, and families suggested design ideas for supporting this context, such as UI to manage conversational flow and reduce competition, and visuals and embodied activities to encourage focus. The dual-user context coupled with the audio modality prompted “preinput huddles” in which children conversed in whispers before responding to the system. We contribute evidence that CAs can support children in learning to use self-talk as well as design guidance for creating multi-user conversational interfaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".