Application of artificial intelligence in early childhood development: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Early childhood-specifically, the period from 0 to 6 years of age-is a critical time in children's lives with rapid growth in their cognitive, social and emotional development. This period has also been shown to be the most effective time for early interventions. The use of artificial Intelligence (AI) for supporting early child development is increasing alongside the rapid advancement of technology. AI can be used directly by children (eg, for implementing adaptive technologies), by individuals who interact with children (eg, educators, parents, nurses), and by individuals indirectly supporting early child development (eg, early childhood researchers or policy analysts). This scoping review will provide a roadmap for relevant stakeholders on how AI has been applied within and across different contexts to support infants and young children's development, as well as the most predominant AI technologies used across various contexts. METHODS AND ANALYSIS: The current study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review The search syntax will be applied in PsycINFO, ERIC, Education Source, CINHAL, MEDLINE, Embase and IEEE Xplore. The purpose of this study is to curate and synthesise academic papers to examine the application of AI for supporting the development of children between birth and age 6 years of age. Studies with children or individuals who work directly or indirectly with children will be included. Part of the abstract and full-text screening will be conducted by two researchers, with discrepancies being resolved by the lead authors. In addition, AI will be used to help with study screening and data extraction once confirmed to be reliable (Cohen's kappa >0.80). Thematic and content analyses will be conducted to identify the types of AI products used and their applications in different contexts, the most predominant AI products used within and across each context, as well as how children's developmental outcomes are impacted by the use of these AI products. Where applicable, visualisations such as tables, graphs and figures will be used to synthesise the data across contexts and AI products used to support early development of young children.
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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 | 0.094 | 0.088 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.018 |
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".