Application of machine learning in early childhood development research: a scoping review.

Journal: BMJ open
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential. Traditional measures fail to fully capture the risks associated with a child's development outcomes. Artificial intelligence techniques, particularly machine learning (ML), offer an innovative approach by analysing complex datasets to detect subtle developmental patterns.

Authors

  • Faith Neema Benson
    Institute for Human Development, The Aga Khan University, Nairobi, Kenya faith.neema@aku.edu.
  • Daisy Chelangat
    Institute for Human Development, The Aga Khan University, Nairobi, Kenya.
  • Willie Brink
    Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa.
  • Patrick N Mwangala
    Institute for Human Development, The Aga Khan University, Nairobi, Kenya.
  • Akbar K Waljee
    VA Center for Clinical Management Research, VA Ann Arbor Health Care System, Ann Arbor, Michigan.
  • Cheryl A Moyer
    Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.
  • Amina Abubakar
    Institute for Human Development, The Aga Khan University, Nairobi, Kenya.

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