Clinical decision making sits at the core of safe and effective medical practice. It goes beyond factual knowledge and requires learners to synthesize information, manage uncertainty, prioritize actions, and adapt as patient conditions evolve. For medical instructors, improving clinical decision making is not just about teaching more content. It is about measuring the right indicators of thinking and behavior. Traditional assessments often fall short because they focus on out

Artificial intelligence is becoming a foundational component of medical education, but its role differs significantly between preclinical and clinical training. Treating AI as a single solution across all phases of medical education often leads to misalignment and frustration. The most effective programs recognize that AI serves different purposes depending on where learners are in their development. Understanding where AI fits in preclinical versus clinical education allows

Providing timely, meaningful feedback is one of the most impactful aspects of medical education. It shapes clinical reasoning, reinforces correct behaviors, and helps learners course correct before habits become ingrained. Yet as class sizes grow and programs expand, delivering individualized feedback becomes increasingly difficult. Large cohorts place enormous strain on faculty time, often forcing feedback to become delayed, brief, or overly generalized. AI powered education