Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG.

Journal: JMIR formative research
PMID: 40184548

Abstract

BACKGROUND: Template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. Medical education, a field that relies heavily on both formative and summative assessments with multiple choice questions, is in dire need of AI-based support for the efficient automatic generation of items.

Authors

  • Yavuz Selim Kıyak
    Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.
  • Andrzej A Kononowicz
    Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna Str 7, Kraków, 30-688, Poland, 48 12 3476908.

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