医学教育中的真实评估:探索人工智能整合与学生作为合作伙伴的合作。

IF 3.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL Postgraduate Medical Journal Pub Date : 2024-07-23 DOI:10.1093/postmj/qgae088
Syeda Sadia Fatima, Nabeel Ashfaque Sheikh, Athar Osama
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引用次数: 0

摘要

背景:传统的评估往往缺乏灵活性、个性化反馈、现实世界的适用性,以及衡量死记硬背之外的技能的能力。它们可能无法充分适应不同的学习风格和偏好,也不能始终促进批判性思维或创造力。将人工智能(AI),尤其是生成式预训练变形器(Generative Pre-trained Transformers)纳入医学教育,标志着一个重大转变,既为真实的评估实践提供了令人兴奋的机遇,也带来了显著的挑战。预计包括解剖学、生理学、药学、牙医学和病理学在内的各个领域将越来越多地采用元宇宙进行真实评估。这种创新方法将有可能使学生参与到身临其境、基于项目的学习体验中,促进跨学科合作,并为知识和技能在现实世界中的应用提供一个平台:本评论文章探讨了人工智能、真实评估和学生即合作伙伴(SaP)方法如何共同重塑医学教育中的评估实践:本文就如何有效利用人工智能工具创建真实的评估提供了实用的见解,为教育工作者提供了可操作的指导,以加强他们的教学实践。论文还探讨了在实施人工智能驱动的评估过程中固有的挑战和道德考量,强调了在医学教育中采取负责任和包容性做法的必要性。该评论提倡人工智能与SaP方法之间的合作,并提出了一个强有力的计划,以确保道德使用,同时维护学术诚信:通过引导新兴的评估范式,促进对医学知识和能力的真正评估,这一合作努力旨在提升医学教育的质量,使学习者更好地为复杂的临床实践做好准备。
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Authentic assessment in medical education: exploring AI integration and student-as-partners collaboration.

Background: Traditional assessments often lack flexibility, personalized feedback, real-world applicability, and the ability to measure skills beyond rote memorization. These may not adequately accommodate diverse learning styles and preferences, nor do they always foster critical thinking or creativity. The inclusion of Artificial Intelligence (AI), especially Generative Pre-trained Transformers, in medical education marks a significant shift, offering both exciting opportunities and notable challenges for authentic assessment practices. Various fields, including anatomy, physiology, pharmacy, dentistry, and pathology, are anticipated to employ the metaverse for authentic assessments increasingly. This innovative approach will likely enable students to engage in immersive, project-based learning experiences, facilitating interdisciplinary collaboration and providing a platform for real-world application of knowledge and skills.

Methods: This commentary paper explores how AI, authentic assessment, and Student-as-Partners (SaP) methodologies can work together to reshape assessment practices in medical education.

Results: The paper provides practical insights into effectively utilizing AI tools to create authentic assessments, offering educators actionable guidance to enhance their teaching practices. It also addresses the challenges and ethical considerations inherent in implementing AI-driven assessments, emphasizing the need for responsible and inclusive practices within medical education. Advocating for a collaborative approach between AI and SaP methodologies, the commentary proposes a robust plan to ensure ethical use while upholding academic integrity.

Conclusion: Through navigating emerging assessment paradigms and promoting genuine evaluation of medical knowledge and proficiency, this collaborative effort aims to elevate the quality of medical education and better prepare learners for the complexities of clinical practice.

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来源期刊
Postgraduate Medical Journal
Postgraduate Medical Journal 医学-医学:内科
CiteScore
8.50
自引率
2.00%
发文量
131
审稿时长
2.5 months
期刊介绍: Postgraduate Medical Journal is a peer reviewed journal published on behalf of the Fellowship of Postgraduate Medicine. The journal aims to support junior doctors and their teachers and contribute to the continuing professional development of all doctors by publishing papers on a wide range of topics relevant to the practicing clinician and teacher. Papers published in PMJ include those that focus on core competencies; that describe current practice and new developments in all branches of medicine; that describe relevance and impact of translational research on clinical practice; that provide background relevant to examinations; and papers on medical education and medical education research. PMJ supports CPD by providing the opportunity for doctors to publish many types of articles including original clinical research; reviews; quality improvement reports; editorials, and correspondence on clinical matters.
期刊最新文献
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