This practical guide by Daniel A. Hashimoto, MD; Julian Varas, MD; and Todd A. Schwartz, DrPH, provides an overview of the application of artificial intelligence (AI) and machine learning (ML) in surgical education research. The introduction highlights the growing interest in AI due to advancements in computing power and data access, emphasizing the potential of AI in surgical education. However, the current infrastructure and practices in data management and analysis need improvement to support rigorous research. The guide outlines the broad range of AI applications in surgical education, from quantitative data analysis to qualitative and visual data. It stresses the importance of multidisciplinary collaboration with statisticians and computer scientists to ensure rigorous investigation and appropriate interpretation of results. The focus is primarily on ML methods, which can handle complex relationships and non-tabular data such as images and videos. Key considerations include defining clear research questions, ensuring sufficient sample sizes, exploring data thoroughly, and interpreting results within the context of the data. The guide also addresses the limitations of ML methods, such as the lack of transparency and the need for high-quality annotations. It emphasizes the importance of using appropriate evaluation metrics and following rigorous reporting standards for AI research. The article concludes with recommendations for finding more information on ML methodology and references to relevant literature and resources.This practical guide by Daniel A. Hashimoto, MD; Julian Varas, MD; and Todd A. Schwartz, DrPH, provides an overview of the application of artificial intelligence (AI) and machine learning (ML) in surgical education research. The introduction highlights the growing interest in AI due to advancements in computing power and data access, emphasizing the potential of AI in surgical education. However, the current infrastructure and practices in data management and analysis need improvement to support rigorous research. The guide outlines the broad range of AI applications in surgical education, from quantitative data analysis to qualitative and visual data. It stresses the importance of multidisciplinary collaboration with statisticians and computer scientists to ensure rigorous investigation and appropriate interpretation of results. The focus is primarily on ML methods, which can handle complex relationships and non-tabular data such as images and videos. Key considerations include defining clear research questions, ensuring sufficient sample sizes, exploring data thoroughly, and interpreting results within the context of the data. The guide also addresses the limitations of ML methods, such as the lack of transparency and the need for high-quality annotations. It emphasizes the importance of using appropriate evaluation metrics and following rigorous reporting standards for AI research. The article concludes with recommendations for finding more information on ML methodology and references to relevant literature and resources.