中国医学生理学教育中的人工智能基础:教学实践与系统挑战。

IF 1.7 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Advances in Medical Education and Practice Pub Date : 2025-08-15 eCollection Date: 2025-01-01 DOI:10.2147/AMEP.S532951
Haoran Li
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引用次数: 0

摘要

将人工智能(AI)整合到中国的医学生理学教育中已经成为一种变革战略,以实现教学实践的现代化,并解决医疗保健培训中的系统性挑战。目前的计划利用人工智能驱动的工具,如用于生理模拟的机器学习算法和用于沉浸式临床培训的虚拟现实(VR),旨在标准化教育成果,提高学生参与度,并提高对复杂临床场景的准备。然而,人工智能的快速采用带来了严峻的挑战,包括由于过度依赖技术而扩大城乡机构之间的资源差距,教育和临床数据管理中的数据隐私风险,以及培训环境中人文关怀的潜在侵蚀。对学术诚信的担忧进一步加剧了这些挑战——学生使用大型语言模型(llm)来代替评估中的批判性思维——以及平衡人工智能效率与传统教学方法的需要,特别是在像中医(TCM)这样的专业领域,师徒模式仍然是基础。为了协调技术创新与教育诚信,本综述提出了一个平衡的框架,其中包括五个关键战略:适应性课程设计,将人工智能工具与人类监督协同起来;道德治理,确保算法透明度和数据安全;公平的资源分配,弥合地区差距;教师发展计划,提高人工智能素养;以及将人工智能与个性化指导相结合的导师生态系统。通过将人工智能的潜力与核心教学价值相协调,这些战略旨在培养新一代的临床医生,他们既具备技术水平,又具备道德眼光,最终提高中国的医疗质量和可及性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Foundations in China's Medical Physiology Education: Pedagogical Practices and Systemic Challenges.

The integration of artificial intelligence (AI) into China's medical physiology education has emerged as a transformative strategy to modernize pedagogical practices and address systemic challenges in healthcare training. Current initiatives leverage AI-driven tools such as machine learning algorithms for physiological simulations and virtual reality (VR) for immersive clinical training, aiming to standardize educational outcomes, enhance student engagement, and improve readiness for complex clinical scenarios. However, the rapid adoption of AI introduces critical challenges, including widening resource disparities between urban and rural institutions due to over-reliance on technology, risks to data privacy in educational and clinical data management, and potential erosion of humanistic care in training environments. These challenges are further compounded by concerns over academic integrity-evidenced by student use of large language models (LLMs) to substitute critical thinking in assessments-and the need to balance AI efficiency with traditional teaching methods, particularly in specialized fields like Traditional Chinese Medicine (TCM) where master-apprentice models remain foundational. To reconcile technological innovation with educational integrity, this review proposes a balanced framework encompassing five key strategies: adaptive curriculum design that synergizes AI tools with human oversight, ethical governance to ensure algorithmic transparency and data security, equitable resource distribution to bridge regional gaps, faculty development programs to enhance AI literacy, and mentorship ecosystems that integrate AI with personalized guidance. By harmonizing AI's potential with core pedagogical values, these strategies aim to cultivate a new generation of clinicians equipped with both technical proficiency and ethical discernment, ultimately advancing healthcare quality and accessibility across China.

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来源期刊
Advances in Medical Education and Practice
Advances in Medical Education and Practice EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
3.10
自引率
10.00%
发文量
189
审稿时长
16 weeks
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