本文探索了生成式人工智能技术在生物医学基础课程“解剖与生理”中的应用与风险控制机制,旨在构建具有伦理敏感性、结构化逻辑和教学实效性的融合教学体系。研究首先构建了基于GB/T 27921-2023标准的伦理风险识别与评估框架,明确了课堂教学中数据泄露、信息偏差、能力退化、教师弱化等主要伦理风险源,创新性绘制了伦理风险矩阵,为医学教育生成式人工智能治理提供了定量化工具支撑;其次,基于“知识-能力-伦理”三维融合理念,提出了结构可视、能力可评、伦理可控的教学策略体系,显著提升学生三维认知精准度与技术批判意识;最后,搭建了虚实融合的电生理实验平台与智能体、知识图谱以及人工智能辅助病例库等工具,实现了实验资源节约与临床思维训练的双重优化。本研究成果具有较强的实践价值、现实意义和推广应用潜力,为医学课程数字化转型和智能化发展提供了可复制的范例支持。
This study systematically explores the application of generative AI technology in the basic biomedical course ″Anatomy and Physiology″, as well as the risk control mechanism. The goal is to develop a teaching system that is ethically sensitive, logically structured, and effective. First, the study developed a ethical risk identification and evaluation framework based on the GB/T 27921-2023 standard, which clarified the main ethical risk sources in the classroom, such as data leakage, information bias, ability degradation, and teacher weakening. This framework also innovatively created an ethical risk matrix, providing a quantitative tool for AI governance in medical education. Second, based on the three-dimensional integration concept of ″knowledge dimension-ability dimension-ethics dimension″, the study proposed a teaching strategy system that is structured, visible, capable of evaluation, and ethical. This system significantly improves students' three-dimensional recognition accuracy and enhances their critical awareness of technology. Finally, the study developed a virtual and physical fusion electric physiology experiment platform that combines intelligent bodies, knowledge graphs, and AI-assisted case libraries. This platform saves experimental resources and optimizes clinical thinking training. The results of this study demonstrate strong practical value, real-world relevance, and potential for widespread application, providing replicable exemplary support for the digital transformation and intelligent development of medical courses.
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