Generative AI is moving quickly into medical education, bringing large language models, virtual patients, adaptive learning platforms, and AI-assisted assessment into the training pipeline. But this wave is not only about new tools—it is changing what educators must be able to do when AI outputs shape clinical reasoning practice, feedback, and learning support.
In traditional settings, medical teachers were largely responsible for knowledge instruction, clinical supervision, and learner assessment. In the GenAI era, expectations expand: educators are increasingly tasked with judging reliability of AI-generated content, orchestrating human–AI collaborative teaching activities, and handling educational and clinical data with robust safeguards.
To address this shift, the China Consortium of Elite Teaching Hospitals published the Consensus on the Digital Intelligence Competency Framework for Medical Teachers in the Chinese Journal of Medical Education Research. The consensus was developed through a structured process led by the consortium and implemented via the “Future Medical Education Leadership Initiative” at The First Affiliated Hospital, Zhejiang University School of Medicine.
The core message is pragmatic: medical teachers need more than tool literacy. They must understand where models fail, supervise AI use critically, and integrate it into curricula only when it strengthens learning outcomes. Rather than turning faculty into AI engineers, the framework positions teachers as clinical educators who can responsibly evaluate algorithmic behavior—especially hallucinations, bias, and limitations tied to training data.
The consensus introduces “digital intelligence competency,” a broader capability set combining digital tool use, ethical judgment, and pedagogical innovation. It emphasizes not only operational proficiency, but also the educator’s ability to protect privacy, disclose AI assistance appropriately, and maintain humanistic ethics in simulations and case-based instruction.
Five core competency domains are outlined: foundational knowledge of generative AI concepts; practical application skills such as prompt strategies and verification workflows; ethics and security covering privacy protection and academic integrity; teaching integration using virtual patients, adaptive systems, and AI-enhanced examinations with learner AI literacy; and research translation for teaching leaders who can convert unmet educational needs into evaluated intelligent solutions.
The framework was shaped with a modified Delphi consultation involving experts from medical education, clinical teaching, teaching management, digital medicine, and AI technology. In two rounds, 45 and 35 valid responses were collected, with high expert authority coefficients and statistically significant agreement.
A notable gap remains in routine educational practice. While many clinicians report using GenAI in clinical work, a substantially smaller share uses it in standardized residency teaching, commonly for content retrieval and material generation. Most recognize ethical concerns and support ethics education within GenAI training—signaling that readiness is less about adoption and more about governance.
As generative AI becomes embedded in health professions education, the central challenge will be supervision and accountability. Medical teachers must be able to test, verify, and contextualize AI outputs so that AI augments—not replaces—professional judgment, clinical reasoning, and ethical formation.
Subject of Research: People
Article Title: Consensus on the Digital Intelligence Competency Framework for Medical Teachers by the China Consortium of Elite Teaching Hospitals
News Publication Date: 20-Jun-2026
Web References: https://rs.yiigle.com/cmaid/1680027
References: https://dx.doi.org/10.3760/cma.j.cn116021-20260406-02299
Image Credits: Not provided
Keywords: Generative AI, medical education, large language models, digital intelligence competency, ethics and security, clinical teaching, AI-assisted assessment
