Actionable companion · Learning design & outcomes

Learning with agency: Leveraging artificial intelligence for the management of preschool wheeze: A narrative review

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Early childhood · preschool. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Management of preschool wheeze is notoriously challenging given heterogeneous clinical trajectories and underlying biological mechanisms dictating therapeutic response. Data‐driven approaches have highlighted the value of identifying individual wheeze phenotypes and underlying biomarkers to support a personalized management approach; however, these advancements have yet to be translated into clinical management. Here, we discuss key opportunities for Artificial Intelligence and Machine Learning to support personalized approaches to wheeze management through vast pattern‐recognition capabilities. Advancements in the development of tools for objective symptom evaluation, remote symptom monitoring, and prediction of clinical trajectories are summarized. Key considerations for the responsible and successful deployment of such promising technologies in real‐world clinical settings are emphasized, including prevention of algorithmic biases, promotion of prediction transparency, and establishing standards for patient data privacy and equitable access to novel technologies.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Early childhood · preschool. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

To further illustrate the potential for AI and ML approaches to address major challenges in the management of early childhood wheeze, we conducted a scoping review of literature spanning from 1987 to 2025 on PubMed and MEDLINE Ovid databases. Keywords used in the search criteria included: “preschool wheeze”, “asthma”, “pediatrics”, “machine learning”, “artificial intelligence”, “artificial intelligence ethics”, and “responsible artificial intelligence”. Reference lists of retrieved studies were also consulted. In total, 508 studies were identified in the initial searches, and 158 were reviewed following abstract and full‐text screening. Referencing the findings of these studies, we summarize the advancements in the application of AI and ML in the management of persistent wheeze, in addition to providing recommendations to ensure the fair and safe application of such tools.

Limitations and uncertainty · source passage

Wheezing, a musical, high‐pitched, and continuous sound emitted from the chest during exhalation 1 is a common objective childhood symptom 2 representing respiratory morbidity. In fact, approximately 50% of children will report at least one episode of wheeze in their first year of life. 3 Recurrent wheeze (1+ documented objective confirmation of wheeze) occurs in approximately one third of preschool‐aged children. 4 Multiple longitudinal cohorts using agnostic data‐driven techniques all describe a “persistent wheeze” phenotype pattern of early life onset wheezing that is highly associated with an asthma diagnosis. 5 , 6 , 7 , 8 , 9 However, these data‐driven approaches have not been translatable to clinic in a prospective fashion. Recently, an expert panel has suggested a standardized definition for “preschool wheeze” 10 that acknowledges the growing body of literature linking early life wheezing (whether transient or not) with long‐term pulmonary sequelae and aims to facilitate future research studies of preschool wheezing syndromes based on “treatable traits”. 11 Management of preschool wheeze in the early childhood or preschool population is a significant healthcare concern due to the substantial burden at both the patient level and healthcare systems level, with asthma representing the most common chronic disease in children. 12

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Measure independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Anglin Dent; Mohammad Kaviul Khan; Padmaja Subbarao. Leveraging artificial intelligence for the management of preschool wheeze: A narrative review. Pediatric Allergy and Immunology (2025-10-01). https://doi.org/10.1111/pai.70207

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.