Beyond Simulation Scripts:A Viewpoint on AI-Driven Digital Standardized Patients in Large-Scale Medical Education (Preprint)

UNSTRUCTURED The rapid advancement of generative artificial intelligence (AI) is transforming medical education, moving beyond digital support toward interactive and adaptive learning environments. Effective medical training requires not only technical proficiency but also communication skills, emotional sensitivity, and ethical judgment. Traditional standardized patients (SPs) have long facilitated experiential learning in these domains, yet they face limitations in scalability, consistency, and sustainability, particularly in large-scale training programs. This Viewpoint introduces AI-driven Digital Standardized Patients (AI-SPs) as a conceptual extension of existing simulation practices. By integrating large language models, affective computing, and personality modeling, AI-SPs generate adaptive, emotionally responsive interactions that simulate clinically meaningful doctor–patient encounters in a repeatable and controllable manner. Drawing on China’s large-scale medical education context, a “Future Learning” framework is proposed, emphasizing personalized, context-sensitive, and emotionally engaging training experiences. We argue that AI-SPs function not merely as technical tools but as pedagogical mediators that reshape relationships among learners, educators, and simulated patients. Ethical considerations, including responsible AI integration and the preservation of humanistic values in medical training, are also highlighted. This perspective underscores the potential of AI-SPs to enhance communication training, humanistic education, and emotional competency development in medical education.