Rethinking Pediatric Human-AI Interaction for Building Safer Digital Health Ecosystems
Bibliographic record
Abstract
Artificial intelligence (AI) systems used in everyday digital spaces often rely on design assumptions shaped by adult patterns of reasoning, which creates specific interpretive gaps for younger users. This editorial examines how narrative-style outputs produced through epistemic automation can make probabilistic estimates appear more authoritative than intended for some adolescents. It also considers how technical opacity and model drift introduce shifts in system behavior that minors may misread as stable clinical logic, since there are few cues that distinguish computational changes from expert reasoning. When adolescents independently consult conversational agents or symptom-oriented tools, these interactions can influence clinical encounters without being systematically discussed. Therefore, this editorial outlines practical ways for clinicians to ask about AI-mediated information seeking and describes developmental design features, such as explicit uncertainty cues, layered explanations, and age-responsive prompting, that can reduce misinterpretation. Treating the pediatric digital ecosystem as a distinct design and regulatory setting allows for more precise alignment between algorithmic behavior, developmental cognition, and clinical 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".