从技术采用者到创造者:利用人工智能辅助氛围编码改变临床教学。

IF 3.3 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Minyang Chow, Olivia Ng
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引用次数: 0

摘要

将理论知识与临床实践所必需的实践技能相结合,仍然是临床教育的一个重大挑战。传统的教学策略往往不能帮助临床医生应对临床决策的不可预测性、紧迫性和多面性,同时也不能为形成独立临床判断所必需的认知启发式的发展提供有限的支持。为了应对这些挑战,我们引入了vibe编码,这是一种新颖的人工智能辅助、无代码开发方法,使教育工作者能够在没有编程专业知识的情况下创建交互式、可定制的学习模拟。通过优先考虑快速原型和迭代改进,vibe编码将重点从技术限制转移到教学目标,允许教育者通过直观的对话提示生成代码。我们应用这种方法开发了两个不同的应用程序:鉴别诊断训练器(DDT),它通过随机临床场景和人工智能生成的反馈来增强诊断推理,以及胰岛素和血糖模拟(IBSS),它提供了代谢动力学的实时探索。这两个工具都是使用人工智能驱动的无代码平台构建的,在可访问性、成本效益和可扩展性方面都有显著改善。我们鼓励教育工作者从技术采用者转变为创造者,利用人工智能驱动的平台开发创新、可扩展和个性化的临床模拟,从而改变学习体验,最终提高患者护理水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From technology adopters to creators: Leveraging AI-assisted vibe coding to transform clinical teaching and learning.

Integrating theoretical knowledge with the practical skills essential for clinical practice remains a significant challenge in clinical education. Conventional teaching strategies often fall short in preparing clinicians to navigate the unpredictable, urgent, and multifaceted nature of clinical decision-making, while also providing limited support for the development of cognitive heuristics essential to forming independent clinical judgment. To address these challenges, we introduce vibe coding, a novel AI-assisted, no-code development approach that enables educators to create interactive, customisable learning simulations without programming expertise. By prioritising rapid prototyping and iterative refinement, vibe coding shifts the focus from technical constraints to pedagogical goals, allowing educators to generate code through intuitive, conversational prompts. We applied this approach to develop two distinct applications: the Differential Diagnosis Trainer (DDT), which enhances diagnostic reasoning through randomised clinical scenarios and AI-generated feedback, and the Insulin and Blood Sugar Simulation (IBSS), which offers real-time exploration of metabolic dynamics. Both tools were built using AI-powered no-code platforms, demonstrating significant improvements in accessibility, cost-effectiveness, and scalability. We encourage educators to transition from technology adopters to creators, leveraging AI-driven platforms to develop innovative, scalable, and personalised clinical simulations that transform learning experiences and ultimately enhance patient care.

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来源期刊
Medical Teacher
Medical Teacher 医学-卫生保健
CiteScore
7.80
自引率
8.50%
发文量
396
审稿时长
3-6 weeks
期刊介绍: Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.
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