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Leveraging large language models to construct feedback from medical multiple-choice Questions.

Mihaela Tomova1, Iván Roselló Atanet2, Victoria Sehy2

  • 1Data-Intensive Systems and Visualization Group (dAI.SY), Fakultät für Informatik und Automatisierung, Technische Universität Ilmenau, Ehrenbergstraße 29, 98693, Ilmenau, Thuringia, Germany. mihaela-todorova.tomova@tu-ilmenau.de.

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Summary
This summary is machine-generated.

Large Language Models (LLMs) can generate valuable content-based feedback for medical exams like the Progress Test Medizin (PTM). This AI-generated feedback, while not perfect, is seen as a useful supplement to traditional numerical scores by medical professionals.

Keywords:
Data analysisFeedbackLarge language modelsMachine learningNatural language processing

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare

Background:

  • Formative assessments, such as the Progress Test Medizin (PTM), can be improved by providing feedback beyond numerical scores.
  • Content-based feedback, derived from exam questions, offers students insights into performance and aids revision.

Purpose of the Study:

  • To explore the utility of Large Language Models (LLMs) in generating content-based feedback for the PTM.
  • To comparatively assess the effectiveness of two LLMs in this task.
  • To gauge medical practitioners' and educators' perceptions of LLM-generated feedback for the PTM.

Main Methods:

  • Utilized two popular LLMs to generate content-based feedback for the PTM.
  • Conducted a comparative assessment of LLM outputs using textual similarity.
  • Administered a survey to medical practitioners and educators regarding LLM feedback utility.

Main Results:

  • Both LLMs demonstrated similar performance, with individual strengths and weaknesses.
  • One LLM, a paid service, produced slightly superior outputs compared to the free alternative.
  • Survey participants found the LLM-generated feedback relevant, useful, and expressed openness to future use.

Conclusions:

  • LLM-generated content-based feedback can be a valuable addition to the numerical feedback currently provided in the PTM.
  • Despite imperfections, LLMs show promise in enhancing medical education assessment tools.
  • Medical professionals are receptive to integrating LLMs into educational feedback mechanisms.