Artificial Intelligence in Medical Education: A Practical Guide for Educators - Nivritti Gajanan Patil; Nga Lok Kou; Daniel T. Baptista‐Hon; Olivia Monteiro (MedComm – Future Medicine, 2025)
ABSTRACT Artificial intelligence (AI)‐driven learning is transforming education, requiring educators to quickly develop the skills to integrate AI tools effectively so they complement rather than replace traditional teaching practices. The fast pace of generative AI development poses challenges, particularly for less tech‐savvy teachers or those who delay learning about these tools, leaving them at risk of falling behind. This is further compounded by students' quick adaptation to widely available models such as ChatGPT‐3.5 and Deepseek R1, which they increasingly use for learning, assignments, and assessments. Despite existing discussions on AI in education, there is a lack of practical guidance on how medical educators can effectively and responsibly implement AI tools in teaching. This perspective provides a practical guide for medical educators to effectively incorporate AI tools to complement their teaching strategies, generate student assessments and to adapt assignments suitable for the AI era. We address challenges such as data bias, accuracy, and ethics, ensuring AI enhances rather than undermines medical training when aligned with sound pedagogical principles. This review provides a practical, structured approach for educators, offering clear recommendations to help bridge the gap between AI advancements and effective teaching methodologies in medical education.