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Artificial intelligence

Artificial Intelligence in Early Childhood Development: A Scoping Review of Machine Learning Techniques

Strategic Insights

— 1 min read

Research from the University of Michigan reveals that artificial intelligence techniques, particularly machine learning, offer innovative approaches to analyzing complex datasets in early childhood development, helping to detect subtle developmental patterns. The study mapped the existing literature on the use of machine learning in ECD research, identifying research gaps and future directions. The review highlights the need for inclusive, interpretable, and externally validated models that can be integrated into real-world implementation.

Key Takeaways:

  • Of the 759 articles retrieved, 27 met the inclusion criteria, with most studies originating from high-income countries (78%) and none from sub-Saharan Africa.
  • Supervised ML classifiers (40.7%) and deep learning techniques (22.2%) were the most used approaches in ECD research.
  • Cognitive development was the most frequently targeted outcome (33.3%), often measured using the Bayley Scales of Infant and Toddler Development-III (33.3%).
  • Key predictive features were grouped into six categories: brain features; anthropometric and clinical/biological markers; socio-demographic and environmental factors; medical history and nutritional indicators; linguistic and expressive features; and motor indicators.
  • Most studies (74.1%) focused solely on prediction, with the majority conducting predictions at age 2 years and above.
  • Only 41% of studies employed explainability methods, and validation strategies varied widely, with few studies (7.4%) conducting external validation.

Statistics:

  • 759 articles were retrieved from the systematic search.
  • 27 articles met the inclusion criteria.
  • 78% of studies originated from high-income countries.
  • 40.7% of studies used supervised ML classifiers.
  • 22.2% of studies used deep learning techniques.
  • 33.3% of studies targeted cognitive development.
  • 33.3% of studies used the Bayley Scales of Infant and Toddler Development-III.
  • 74.1% of studies focused solely on prediction.
  • 41% of studies employed explainability methods.

Sources:

  • Application of machine learning in early childhood development research: a scoping review. BMJ Open, 2025,15(8). (BMJ Open - http://bmjopen.bmj.com/).
  • Findings in Machine Learning Reported from University of Michigan (Application of machine learning in early childhood development research: a scoping review). Health & Medicine Week. September 5, 2025; p 1707.

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