Training scalability in surgical education is challenged by the limited availability of expert instructors and the need for personalized feedback. The integration of artificial intelligence (AI) into surgical training offers promising solutions to these challenges. This narrative review examines how AI-based tools enhance surgical training scalability, focusing on automated assessments, feedback delivery, and simulation-based education. A comprehensive literature search identified relevant studies on AI applications in surgical training. The review discusses the educational foundations of simulation training, defines AI and its subsets (machine learning and deep learning), and explores the significance of phase/task segmentation in surgical procedures. It critically analyzes current AI applications in automated assessment and feedback, highlighting impediments to scalability such as the specificity of AI models to particular procedures and the need for new models across different domains. The findings suggest that while AI holds significant potential for improving surgical education, challenges remain in generalizing models and integrating AI tools into diverse training contexts.