在元宇宙医学与医学新质生产力的双重驱动下，呼吸病学教学亟需从传统的“知识讲授与指南复述”模式，升级 为面向未来的“AI+呼吸病学新质生产力中心”—兼具教学原型探索与复合型人才孵化功能的前沿阵地。新时代的呼吸科医 生，除扎实的临床专业能力外，还需具备数字健康胜任力，能够批判性地理解、评估并合理应用人工智能（AI）、物联网（IoT）、数 字孪生（digital twin）及扩展现实（extended reality， XR）等新兴技术，在人机协同的多学科诊疗团队（MDT）与“数字人专家”辅助 框架下，开展安全可靠的临床决策、风险分层评估与治疗方案推演。国际循证教育研究显示，AI已在医学影像判读、临床技能 评估、自适应学习与即时反馈等场景中初步应用，但多呈现为孤立的“点状创新”，尚未形成跨学科融合、结构化设计与治理机 制前置的一体化课程体系。呼吸病学因其天然的多模态特征与高度可视化的过程数据（涵盖影像学、肺功能、血气分析、症状 动态轨迹、可穿戴设备监测指标、机械通气参数及睡眠监测数据等），是最适宜通过数字孪生与沉浸式XR场景，系统性弥补传 统课堂“不可见、难模拟、难推演”等核心短板的学科领域。本方案以国际数字健康与AI胜任力共识为引领，系统对标《欧洲医 生数字教育能力成果框架》（DECODE）与《医学教育最佳证据协作组》（BEME）等权威教育框架，构建“知识—工具—临床推理 —伦理治理”四维教学目标。课程以“AI+病例链”为核心进行模块化重构：课前通过诊断式预习与学习画像精准识别认知起 点；课中融合多模态数据可视化与对话式标准化病人（或交互式智能病例），实现沉浸式、情境化的临床思维训练；课后依托学 习分析技术推动个性化反馈，并反哺科研创新，形成“教—学—研”有机闭环。在评价与保障机制上，采用基于客观结构化临床 考试（OSCE）的多元评估体系，坚持“AI支撑、绝不替考”原则，同时将伦理合规与可信AI治理（包括数据隐私保护、算法偏倚防 控、人机责任边界界定及学术诚信规范）作为教学底线。最终，依托跨学科创新中心与可复用的数字基础设施，推动传统课堂 向“可视化呈现、深度互动、推理驱动”的新型智慧教学模式转型，系统培养面向2035年“智慧呼吸中心”与“元宇宙医学实践” 的先锋种子团队。

Driven by the converging forces of metaverse medicine and the new quality productive forces in healthcare, respiratory medicine education must evolve from the traditional model of ‘knowledge delivery and guideline recitation’ toward a forward-looking ‘AI+ Respiratory New-Quality Productive Center’ - a pioneering hub that integrates pedagogical prototyping with the incubation of interdisciplinary talent. Contemporary respiratory physicians, beyond mastering core clinical competencies, must also cultivate digital health literacy: the ability to critically understand, evaluate, and appropriately apply emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), digital twins, and extended reality (XR). Within human - AI collaborative multidisciplinary teams (MDTs) and frameworks augmented by ‘digital expert avatars,’ they should be empowered to make safe clinical decisions, conduct risk stratification, and simulate therapeutic strategies. International evidence-based medical education research indicates that AI has already been piloted in areas such as radiological interpretation, clinical skills assessment, adaptive learning, and real-time feedback. However, these efforts largely remain fragmented ‘point innovations’, lacking an integrated curriculum that embeds cross-disciplinary integration, structured design, and proactive governance. Respiratory medicine - by virtue of its inherently multimodal nature and richly visualizable longitudinal data (including imaging, pulmonary function tests, blood gas analysis, dynamic symptom trajectories, wearable-derived metrics, ventilator parameters, and sleep monitoring data) - is uniquely positioned to leverage digital twin and immersive XR scenarios to systematically address the core limitations of conventional classrooms: phenomena that are ‘invisible, difficult to simulate, and hard to reason through.’ This proposal is grounded in international consensus on digital health and AI competencies, systematically aligning with authoritative frameworks such as the Digital Education Competency Outcomes for Doctors in Europe (DECODE) and the Best Evidence Medical Education (BEME) Collaboration. It establishes a four-dimensional instructional framework encompassing Knowledge - Tools - Clinical Reasoning - Ethical Governance. At its core is an ‘AI + Case Chain’ modular curriculum: Pre-class: diagnostic pre-assessments and learner profiling to identify cognitive baselines; In-class: multimodal data visualization integrated with conversational standardized patients or interactive intelligent cases to enable immersive, context-rich clinical reasoning training; Post-class: learning analytics-driven personalized feedback loops that feed back into scholarly inquiry, creating a seamless teaching - learning- research cycle. Assessment and safeguard mechanisms center on a multi-modal evaluation system based on Objective Structured Clinical Examinations (OSCEs), adhering strictly to the principle of ‘AI-supported, never AI-substituted’ assessment. Ethical compliance and trustworthy AI governance -including data privacy protection, algorithmic bias mitigation, clear delineation of human -AI responsibility boundaries, and academic integrity - are embedded as non-negotiable safeguards. Ultimately, by leveraging interdisciplinary innovation centers and reusable digital infrastructures, this initiative transforms the traditional classroom into a next-generation smart learning environment characterized by visualizability, deep interactivity, and reasoning-driven pedagogy, systematically cultivating a vanguard cohort of talent ready to lead ‘smart respiratory care centers’ and ‘metaverse medicine’ practices by 2035.