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

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.