Social Emotional Learning with Conversational Agents
Bibliographic record
Abstract
Social emotional skills are foundational competencies upon which children draw throughout their lives. This work investigates current, commercially available experiences for social emotional learning (SEL) through conversational agents (CAs). Specifically, we reviewed 3,767 Skills available in the "Kids" category of the Alexa Skills Marketplace and found 42 working Skills with connections to SEL. We found that the most common scenarios these Skills sought to support were: active listening, emotional wellbeing, conversation with other people, and politeness. The interaction patterns used by these Skills distilled into a taxonomy of styles we labeled: The Delegator, The Lecturer, The Bulldozer, and The One-Track Mind. We found that, collectively, these Skills provide shallow experiences and lack contingent feedback. To examine the gap between current offerings and families' needs, we also conducted 26 interviews with parents to probe parents' ideas about CAs supporting children's SEL. Parents see potential for CAs to support children in four concrete ways, including attuning to others, cultivating curiosity, reinforcing politeness, and developing emotional awareness. Despite their optimism about these opportunities, parents expressed skepticism about CAs' impoverished conversational abilities and worry about CAs advancing values and behavioral norms that are at odds with their own.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".