Allison Mool, Jacob Schmid, Thomas Johnston, William Thomas, Emma Fenner, Kevin Lu, Raya Gandhi, Adam Western, Brendan Seabold, Kodi Smith, Zachary Patterson, Hannah Feldt, Daniel Vollmer, Roshan Nallaveettil, Anthony Fanelli, Logan Schmillen, Shelley Tischkau, Anna T. Cianciolo, Pinckney Benedict, Richard Selinfreund
{"title":"Using Generative AI to Simulate Patient History-Taking in a Problem-Based Learning Tutorial: A Mixed-Methods Study","authors":"Allison Mool, Jacob Schmid, Thomas Johnston, William Thomas, Emma Fenner, Kevin Lu, Raya Gandhi, Adam Western, Brendan Seabold, Kodi Smith, Zachary Patterson, Hannah Feldt, Daniel Vollmer, Roshan Nallaveettil, Anthony Fanelli, Logan Schmillen, Shelley Tischkau, Anna T. Cianciolo, Pinckney Benedict, Richard Selinfreund","doi":"10.1101/2024.05.02.24306753","DOIUrl":null,"url":null,"abstract":"<strong>Background</strong> Medical educators who implement problem-based learning (PBL) strive to balance realism and feasibility when simulating patient cases, aiming to stimulate collaborative group discussion, engage students’ clinical reasoning, motivate self-directed learning, and promote the development of actionable scientific understanding. Recent advances in generative artificial intelligence (AI) offer exciting new potential for patient simulation in PBL","PeriodicalId":501387,"journal":{"name":"medRxiv - Medical Education","volume":"117 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Medical Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.05.02.24306753","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background Medical educators who implement problem-based learning (PBL) strive to balance realism and feasibility when simulating patient cases, aiming to stimulate collaborative group discussion, engage students’ clinical reasoning, motivate self-directed learning, and promote the development of actionable scientific understanding. Recent advances in generative artificial intelligence (AI) offer exciting new potential for patient simulation in PBL
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