Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan

The study demonstrated the feasibility and utility of using PAML to identify at-risk learners and tailor support strategies for enhancing educational outcome in low-resource settings. This additional support can augment expert judgement and ensure equitable educational practices. However, model limitations and ethical concerns, such as algorithmic bias, overfitting, and data imbalance, must be actively addressed in high-stakes assessments.[Box: see text].