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Related Experiment Video

Updated: May 6, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Is your curriculum GenAI-proof? A method for GenAI impact assessment and a case study.

Remco Jongkind1, Erik Elings1, Erik Joukes2

  • 1Teaching and Learning Centre, Amsterdam UMC Location AMC, Amsterdam, North Holland, 1105AZ, The Netherlands.

Mededpublish (2016)
|March 13, 2026
PubMed
Summary
This summary is machine-generated.

Generative AI (GenAI) poses risks to educational assessments. A new method evaluated GenAI's impact on assessment validity and learning outcomes, leading to adaptations in 64% of assessments and 36% of learning outcomes.

Keywords:
AI literacyChatGPTCurriculum adaptationGenAIGenerative AILarge Language Modelsassessment validitylearning outcome relevance.

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Area of Science:

  • Medical Informatics
  • Educational Technology
  • Artificial Intelligence in Education

Background:

  • Generative AI (GenAI) presents both opportunities and risks in education, particularly concerning assessment validity and learning outcomes.
  • Student use of GenAI tools like ChatGPT can undermine traditional assessment methods.
  • A systematic approach is needed to evaluate and address the impact of GenAI on educational curricula.

Purpose of the Study:

  • To present a broadly applicable method for determining assessment validity in the context of GenAI.
  • To assess the impact of student GenAI use on learning outcomes.
  • To provide a framework for deciding whether to alter assessment formats and/or learning outcomes.

Main Methods:

  • A five-step method was developed and applied to a medical informatics curriculum.
  • Collaborative analysis of course assessment plans with instructors.
  • Adaptation of course assessments and learning outcomes based on GenAI impact evaluation.

Main Results:

  • 57% of assessments, particularly in writing and programming, were at risk of reduced validity and relevance.
  • GenAI's impact on validity was linked to assessment content and structure, not complexity.
  • 64% of impacted assessments had their formats adapted, while 36% had learning outcomes altered.

Conclusions:

  • The majority of assessments were affected by GenAI, necessitating adaptations to assessments or learning outcomes.
  • The developed method and case study provide a blueprint for educational institutions addressing GenAI challenges.
  • Adapting assessments or learning outcomes is crucial for maintaining educational integrity in the age of GenAI.