This guide is a roadmap for medical educators and researchers who want to use artificial intelligence (AI) in training or assessment. It explains key AI concepts in plain language, shows which educational challenges and types of data are well-suited for AI, and highlights practical considerations for reading or conducting AI research in medical education. The authors stress that AI can help with tasks like grading open-ended answers, giving personalized feedback, or analyzing simulations things that would normally take a lot of time and human resources.

At the same time, the guide cautions that AI isn’t a one-size-fits-all solution. Not every problem or dataset is appropriate, and careful planning is needed to avoid bias, ensure fairness, and maintain the validity of educational assessments. Essentially, the paper encourages educators to adopt AI thoughtfully: using it where it makes sense, understanding what it can and cannot do, and making sure it genuinely enhances learning rather than just adding technology for its own sake.