Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesiology work offers some protections from AI clinical assimilation, this technology will ultimately impact the practice and augment the ability to provide an enhanced level of safe and data-driven care. Whether predicting difficulties with airway management, providing perioperative or critical care risk assessments, clinical-decision enhancement, or image interpretation, the indications for AI technologies will continue to grow and are limited only by our collective imagination on how best to deploy this technology.An essential mission of academia is education, and challenges are frequently encountered when working to develop and implement comprehensive and effectively targeted curriculum appropriate for the diverse set of learners assigned to teaching faculty. Curriculum development in this context frequently requires substantial efforts to identify baseline knowledge, learning needs, content requirement, and education strategies. Large language models offer the promise of targeted and nimble curriculum and content development that can be individualized to a variety of learners at various stages of training. This technology has not yet been widely evaluated in the context of education deployment, but it is imperative that consideration be given to the role of AI in curriculum development and how best to deploy and monitor this technology to ensure optimal implementation.