Artificial intelligence, particularly large language models, can support scenario generation, virtual patients, personalized learning, and feedback in pediatric emergency education. However, the time-sensitive and age-dependent nature of pediatric emergency care requires rigorous evaluation of these tools for accuracy, safety, and educational effectiveness. Key limitations include plausible misinformation, omission of critical actions, inconsistent responses, algorithmic bias, and risks to patient confidentiality. Strong performance on examinations does not establish practical competence or improved patient outcomes. This letter reviews evidence from medical education and pediatric resuscitation and proposes a staged validation framework encompassing the intended use and target learners, expert content review, response consistency, generalizability, assessment validity, learning retention and transfer, and data governance. Responsible integration requires faculty oversight and continuous monitoring, positioning artificial intelligence as a complement to clinical teaching and simulation rather than a replacement for professional judgment and hands-on practice.