Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study.

MedEdPublish (2016) Pub Date : 2026-04-28 eCollection Date: 2025-01-01 DOI:10.12688/mep.20815.3
Remco Jongkind, Erik Elings, Erik Joukes, Tom Broens, Hemmik Leopold, Floris Wiesman, Jennita Meinema
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Abstract

Background: Generative AI (GenAI) can complete tasks that previously could only be performed successfully by humans. Although GenAI provides many opportunities, it also poses risks such as invalid assessments and irrelevant learning outcomes. This article presents a broadly applicable method to (1) determine current assessment validity, (2) assess which learning outcomes are impacted by student GenAI use and (3) decide whether to alter assessment formats and/or learning outcomes. This is exemplified by a case-study on our Medical Informatics curriculum. We developed a five-step method to evaluate and address the impact of GenAI. Together with the respective lecturers, all assessments in the courses comprising a curriculum are analysed and subsequently adapted to address the impact of GenAI usage.

Results: 57% (55/96) of assessments, especially for courses involving writing and programming, were at risk of reduced validity and relevance. GenAI impact on assessment validity was closer related to the content and structure of assessments than to their complexity according to Bloom's taxonomy. During educational retreats, lecturers discussed the relevance of impacted learning outcomes and whether students should be able to achieve them with or without GenAI. Furthermore, the results led to a plan to increase GenAI literacy and the intentional use by students during their education. For this purpose, course coordinators were asked to either adjust their manner of assessment to preclude GenAI use, or to alter learning outcomes to include GenAI use and literacy. For 64% of the impacted assignments (35/55) the assessment format was adapted and for 36% (20/55) the learning outcomes were adapted.

Conclusion: The majority of assessments in our curriculum were at risk of reduced assessment validity and relevance of learning outcomes, necessitating adaptations of either the manner of the assessment or learning outcomes. The proposed method and case-study offer a replicable framework for educational institutions facing similar challenges.

你的课程是基因认证的吗?GenAI影响评估方法及案例研究。
背景:像ChatGPT这样的生成式人工智能(GenAI)可以接管以前只能由人类完成的任务。尽管GenAI提供了许多教育机会,但它也带来了诸如无效评估和不相关的学习结果等风险。本文提出了一种广泛适用的方法来(1)确定当前评估的有效性,(2)评估哪些学习成果受到学生使用GenAI的影响,(3)决定是否改变评估格式和/或学习成果。我们的医学信息学课程的案例研究说明了这一点。我们开发了一种评估和解决GenAI影响的五步方法。以合作的方式,对课程中的课程根据其评估计划进行分析,并与教师一起对课程进行调整,以解决使用GenAI的影响。结果:57%的评估,特别是在写作和编程方面,存在有效性和相关性降低的风险。根据Bloom的分类法,基因对评估效度的影响与评估的内容和结构的关系比评估的复杂性更密切。在教育静修期间,讲师讨论了受影响的学习成果的相关性,以及学生是否应该能够在有或没有GenAI的情况下实现这些成果。此外,研究结果还导致了一项计划,即在多年的研究中提高GenAI的识字率和使用率。随后,协调员被要求调整他们的评估格式以排除GenAI的使用,或者改变学习结果,包括GenAI的使用和读写能力。对64%的影响评估进行了调整,对36%的学习结果进行了调整。结论:我们课程中的大多数评估存在评估有效性和学习成果相关性降低的风险,导致我们对评估或学习成果进行调整。这种方法和案例研究为面临类似挑战的教育机构提供了潜在的蓝图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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