Ocular Component Growth From the Cornea to the Sclera in Non-Human Primate Emmetropization.

Aligning medical school curricula with standardized competency expectations helps students build the knowledge and clinical reasoning skills across the development continuum (preclinical to clinical) and ultimately foster readiness for quality patient care []. For example, high-stakes standardized exams such as the United States Medical Licensing Examination (USMLE) provide a structured framework that guides students to be well-prepared for standardized competency during medical education curricula []. Pharmacology, in particular, plays a crucial role in these curricula due to its direct impact on patient safety and treatment outcomes []. Aligning pharmacological topics taught in medical schools with the USMLE content, which includes a substantial proportion of pharmacology, can help ensure balanced topic coverage, adequate instructional time, and relevant assessment strategies []. However, such alignment tasks are challenging and often require substantial time and effort from curricular teams, which involves coordination and collaboration among multiple stakeholders across the medical education continuum. Moreover, the intricate mechanisms and clinical applications of pharmacology add another layer of difficulty to the task [,]. While traditional alignment methods often rely on faculty expertise only and manually grounded processes, these approaches can be time-intensive and may lack consistency. With the rapid advancements in educational technology, generative artificial intelligence (GenAI), powered by large language models (LLMs) that generate content based on vast amounts of training data, has shown promise in handling large volumes of data and generating situated outputs []. These models possibly assist medical educators in more efficiently aligning curricular content with national competency standards and high-stakes examinations. Yet, concerns remain concerning the reliability, accuracy, and contextual appropriateness of AI-generated materials []. Consequently, there is a growing need to explore how well GenAI models perform complex educational tasks with the same traits as human subject matter experts (human experts).