Artificial intelligence (AI) is the study of machine intelligence as it relates to perceiving and inferring data, typically with the goal of approximating human performance on tasks. Modern growth in computing power and access to data has raised interest in AI, leading to investigation of its applications to surgery. Machine learning (ML), a subfield of AI, refers to the study of methods and algorithms that use data to improve task performance. With the growth of AI in different applications of medicine, there is growing interest in applying AI methods to surgical education research. While the potential for AI to be used in surgical education is high, current infrastructure and practices on data capture, storage, labeling, and analysis leave much to be desired to allow rigorous research using AI methods.1 This practical guide provides an overview of important considerations in using AI techniques and tools in surgical education research and offers a framework to ensure that appropriately designed studies meet expectations for methodologic rigor, particularly regarding use of high-quality data that reflect the phenomenon of interest (Box).