# A Chinese Expert Consensus on the Artificial Intelligence Proficiency of Medical Students: Competencies and the Multi‐Modal Assessment

## TL;DR

This paper presents a Chinese expert consensus on AI literacy for medical students, defining 21 core competencies and a multi-modal assessment framework to integrate AI into medical education.

## Contribution

The study introduces a China-specific AI competency framework and assessment system for medical students, emphasizing localized standards and ethical considerations.

## Key findings

- A 21-item Competencies of AI Proficiency (CAIP) list was developed, covering knowledge, skills, and attitudes.
- A multi-modal assessment system includes paper-based/computerized tests, situational judgment tests (SJT), and OSCEs with an AI Clinical Decision Conflict Scoring Scale.
- The framework recommends longitudinal tracking through a multi-stage dynamic assessment system and modular curriculum integration.

## Abstract

Artificial intelligence (AI) is transforming healthcare, demanding reevaluation of medical education. China's “New Medical Education” initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards, rapid technological evolution, and insufficient localized ethical norms.

To establish a Chinese expert consensus defining core AI competencies and a multi‐modal assessment framework for medical students.

A multidisciplinary (including medical education, clinical medicine, medical AI, public health, and medical ethics) expert group (n = 32) developed an initial competency list based on the “Knowledge‐Skills‐Attitude” Medical Competency Model. Two Delphi rounds (100% response rate; consensus threshold: mean ≥ 4.0, CV ≤ 0.25) refined the framework. Core competencies were prioritized via Analytic Hierarchy Process (AHP). The final consensus document was established after multiple expert group meetings.

The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into professional knowledge, clinical practice, research, and health management. It comprises a 21‐item Competencies of AI Proficiency (CAIP) list across knowledge (eight indicators), skills (seven indicators), and attitude (six indicators) dimensions. Key competencies prioritized include understanding AI's role in multidisciplinary knowledge integration (CAIP3), identifying AI output biases (CAIP4), understanding health data governance (CAIP2), maintaining physician‐led AI‐assisted diagnosis (CAIP16), and identifying AI diagnostic biases (CAIP12). A multi‐modal assessment framework is recommended, including paper‐based/computerized tests for knowledge, situational judgment tests (SJTs) for attitudes, and objective structured clinical examinations (OSCEs) with a specific “AI Clinical Decision Conflict Scoring Scale” for skills. A multi‐stage dynamic assessment system (“Pre‐enrollment–Pre‐clinical–Post‐clinical”) is proposed for longitudinal tracking. Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years, constructing an integrated curriculum covering fundamental principles, advanced large model applications (e.g., prompt engineering, agent development), and ethical considerations, supported by a “digital twin hospital platform.”

This consensus provides authoritative, China‐specific guidance for defining and assessing medical students' AI literacy, adhering to national policies and regulations. It offers a core action framework for optimizing AI integration into medical education, fostering future healthcare professionals proficient in both AI technology and medical humanism, with a commitment to dynamic updating to adapt to evolving AI advancements.

This study proposes an AI literacy framework for medical students, structured around knowledge, skills, and attitudes with 21 core competencies. It stresses physician accountability, and bias vigilance alongside medical humanism. AUnidentified multimodal assessment system is introduced, using tests, SJTs, and OSCEs, with progresss tracked longitudinally. The framework advocates for localized education integrating regulations, a modular curriculum, digital twin training, and alignment with domestic healthcare needs.