Mastery Learning Guided by Artificial Intelligence Is Superior to Directed Self-Regulated Learning in Flexible Bronchoscopy Training: An RCT.

Journal: Respiration; international review of thoracic diseases
PMID: 39419006

Abstract

INTRODUCTION: Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effectiveness of ML compared to directed self-regulated learning (DSRL) on novice bronchoscopists' end-of-training performance.

Authors

  • Kristoffer Mazanti Cold
    Copenhagen Academy for Medical Education and Simulation (CAMES), Rigshospitalet, Copenhagen, University of Copenhagen and the Capital Region of Denmark; kristoffer.mazanti.cold.01@regionh.dk.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
  • Kaladerhan Agbontaen
    Chelsea and Westminster Hospital, Chelsea, London, UK.
  • Suveer Singh
    Chelsea and Westminster Hospital, Chelsea, London, UK.
  • Lars Konge
    Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark.

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