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A generative AI chatbot creates USMLE-style anatomy questions, with 83% deemed usable by experts. This tool offers a cost-effective method for medical education assessment development.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Education
  • Assessment Design

Background:

  • Developing high-quality assessment items for medical education, such as United States Medical Licensing Examination (USMLE)-style anatomy multiple-choice questions, is resource-intensive.
  • Existing assessment creation processes can be time-consuming and costly, potentially limiting local development and equity.

Purpose of the Study:

  • To develop and evaluate a generative artificial intelligence (AI) chatbot for creating curriculum-aligned anatomy multiple-choice questions.
  • To assess the usability and quality of AI-generated questions by subject matter experts.
  • To explore the potential of AI in identifying curricular discrepancies and supporting assessment development in medical education.

Main Methods:

  • A generative AI chatbot was designed to produce USMLE-style anatomy multiple-choice questions using open-access educational resources.
  • One hundred AI-generated questions were reviewed by subject matter experts for usability and required modifications.
  • Sentiment analysis was conducted on interviews with subject matter experts to gauge their perceptions of AI-generated assessment items.

Main Results:

  • 83% of the 100 evaluated AI-generated anatomy questions were found to be potentially usable, with many requiring only minor or no revisions.
  • Sentiment analysis indicated mild positive sentiment among experts, who recognized both the benefits and limitations of AI in assessment creation.
  • The AI chatbot identified discrepancies between faculty expectations and established learning objectives, suggesting areas for curriculum enhancement.

Conclusions:

  • An ethically designed generative AI chatbot offers a scalable and cost-effective solution for creating medical education assessment items.
  • AI-generated questions can support local assessment development, enhance educational equity, and potentially reveal opportunities for curricular improvement.
  • This approach demonstrates the value of AI tools in augmenting traditional methods for medical education and assessment.