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Artificial intelligence (AI) in midwifery education for low and middle-income countries: The opportunities and challenges
Abstract   Open access   Peer reviewed

Artificial intelligence (AI) in midwifery education for low and middle-income countries: The opportunities and challenges

Terri Downer, Ryan M Fraser, Florin Oprescu and Ratan Kumar Roy
European Journal of Midwifery, pp.10-Supplement 1
International Confederation of Midwives (ICM) Triennial Congress, 34th (Lisbon, Australia, 14-Jun-2026–18-Jun-2026)
2026
DOI: https://doi.org/10.18332/052742rioqej
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Abstract-Book-EJM-20264.39 MBDownloadView
Published Version Open Access CC BY V4.0

Abstract

BACKGROUND Artificial intelligence (AI) is increasingly recognised in health professional education, with growing interest in its application within low and middle-income countries. Given the ongoing structural and resource related challenges facing midwifery education in these settings, AI presents a promising opportunity to address these barriers and enhance educational outcomes. OBJECTIVES This study explores the potential opportunities and challenges of integrating AI into midwifery education in low and middleincome countries. METHODS A scoping review was conducted in February 2025 using the Web of Science, PubMed, Scopus and CINAHL databases. Keywords such as “midwifery education” and “artificial intelligence” were used. Articles were screened for relevance and selected studies were categorised according to domains of the Global Standards for Midwifery Education. The results were then analysed to assess their specific implications for midwifery education in low and middle-income country contexts. RESULTS Fifteen papers met the inclusion criteria, including qualitative (n=3) and quantitative (n=2) studies, reviews (n=4), opinion pieces (n=3), oral presentation abstracts (n=2) and an experimental study (n=1). Key opportunities included the ability of AI to reduce educator workload, facilitate personalised learning and enhance simulation-based training. Challenges included ethical concerns, a lack of AI training for educators and students, implementation costs and risks of data bias. CONCLUSIONS AI has the potential to transform midwifery education in low and middle-income countries by addressing persistent barriers and contributing to more equitable health outcomes globally. However, targeted efforts are needed to navigate the associated challenges responsibly. KEY MESSAGE AI offers a transformative opportunity to strengthen midwifery education in low and middle-income countries by addressing workforce shortages and resource gaps, although its success depends on ethical, inclusive and context sensitive implementation.

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