Revolutionizing Nursing Education in Somalia: Recommendations for Integrating AI and Deep Learning in Outcome Based Assessments to Enhance Knowledge, Practice, and Graduate Competence

Abstract

Fathi Yasin Yusuf,1,2 Abdiwali Mohamed Hussein1 1Dr. Sumait Hospital, SIMAD University, Mogadishu, Somalia; 2Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, SomaliaCorrespondence: Fathi Yasin Yusuf, Email fathiyasiny@simad.edu.soAbstract: Artificial intelligence (AI) and deep learning hold significant potential to strengthen outcome-based assessment in Somali nursing education. Somalia’s nursing education system faces critical challenges including inadequate standardization, uneven competency assessment, urban–rural disparities in access, limited faculty capacity, and weak governance. Outcome-based education offers a governance mechanism to define and measure graduate competencies, but its full benefit depends on robust assessment tools. This commentary examines how AI and deep learning can strengthen outcome-based assessment frameworks in Somali nursing schools, identifies the most impactful applications including adaptive learning platforms, AI-enhanced simulation, and automated formative assessment, maps these to Somalia’s documented structural constraints, and proposes context-adapted recommendations for phased, faculty-supervised, and ethically governed implementation. The central argument is that AI and deep learning add the most value when introduced as low-bandwidth, contextually appropriate tools that directly address existing gaps in standardization, feedback, and equitable access rather than as stand-alone technologies.Keywords: artificial intelligence, deep learning, nursing education, outcome-based assessment, Somalia, health professions education

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