Generative AI-Assisted Construction of a PBL Case Library for Acute and Critical Care in the Emergency Department and Its Evaluation in Clinical Teaching

Generative AI-Assisted Construction of a PBL Case Library for Acute and Critical Care in the Emergency Department and Its Evaluation in Clinical Teaching | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Generative AI-Assisted Construction of a PBL Case Library for Acute and Critical Care in the Emergency Department and Its Evaluation in Clinical Teaching Dongyun Chen, Zhiwan Xie, Xuzheng Tang, Yilang Hu, Yu Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7893461/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background：As medical education reform deepens, student-centered PBL is vital in clinical teaching. Yet, ED diseases’ acute, critical nature and traditional PBL’s inability to simulate dynamic changes, lack multidisciplinary scenarios, and misaligned difficulty grading limit its suitability for ED teaching. Objective：This study focuses on applying AI in ED PBL case generation and teaching. It aims to build an AI-enabled PBL system integrating "ED PBL cases – hierarchical question banks – intelligent assessment" via generative AI, to enhance students’ acute illness identification, emergency management, and multidisciplinary collaboration, addressing ED teaching pain points. Methods：A quasi-experimental design with mixed methods was used. 120 Zhenjiang hospital students were randomized into experimental (AI-enabled PBL) and control (traditional PBL) groups. Data included theoretical exams, Clinical Thinking Ability Scale scores, teacher case-design time, and student satisfaction. The Chinese Clinical Thinking Ability Assessment Scale and supervisor evaluations were used. Thematic analysis handled qualitative data; independent samples t-tests and ANOVA, quantitative data. Results：The AI-enabled model outperformed traditional PBL. Experimental group’s theoretical exam score (84.3 ± 5.2) was higher than control’s (76.8 ± 6.1, t = 6.21, P < 0.001). Its CCTS score (78.5 ± 7.3) also exceeded control’s (69.2 ± 8.1, t = 5.87, P = 0.003), boosting clinical thinking. Teacher case-design time reduced by 63% (45 ± 12 vs. 122 ± 18 minutes, P < 0.001). Qualitative data revealed positive student experience, teacher recognition of AI question bank difficulty (with optimization needs), and suggestions for improvement plus concerns about AI answer accuracy. Conclusion: The AI-enabled PBL model enhances students’ academic performance, clinical learning and reasoning abilities, and teachers’ instruction quality. It serves as a practical example for medical education digital transformation and is worth wide promotion. Artificial Intelligence Emergency Department PBL Cases Clinical Teaching Intelligent Assessment Constructivist Learning Theory Full Text Additional Declarations No competing interests reported. Supplementary Files S1AppendixQuestionnairesandInterviewGuidesforAIAssistedPBLStudy.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7893461","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543047827,"identity":"101c0727-4016-413d-a1c9-5ab723499c5f","order_by":0,"name":"Dongyun 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Teaching","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Emergency Department, PBL Cases, Clinical Teaching, Intelligent Assessment, Constructivist Learning Theory","lastPublishedDoi":"10.21203/rs.3.rs-7893461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7893461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground：As medical education reform deepens, student-centered PBL is vital in clinical teaching. Yet, ED diseases’ acute, critical nature and traditional PBL’s inability to simulate dynamic changes, lack multidisciplinary scenarios, and misaligned difficulty grading limit its suitability for ED teaching.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eObjective：This study focuses on applying AI in ED PBL case generation and teaching. It aims to build an AI-enabled PBL system integrating \"ED PBL cases – hierarchical question banks – intelligent assessment\" via generative AI, to enhance students’ acute illness identification, emergency management, and multidisciplinary collaboration, addressing ED teaching pain points.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods：A quasi-experimental design with mixed methods was used. 120 Zhenjiang hospital students were randomized into experimental (AI-enabled PBL) and control (traditional PBL) groups. Data included theoretical exams, Clinical Thinking Ability Scale scores, teacher case-design time, and student satisfaction. The Chinese Clinical Thinking Ability Assessment Scale and supervisor evaluations were used. Thematic analysis handled qualitative data; independent samples t-tests and ANOVA, quantitative data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults：The AI-enabled model outperformed traditional PBL. Experimental group’s theoretical exam score (84.3 ± 5.2) was higher than control’s (76.8 ± 6.1, t = 6.21, P \u0026lt; 0.001). Its CCTS score (78.5 ± 7.3) also exceeded control’s (69.2 ± 8.1, t = 5.87, P = 0.003), boosting clinical thinking. Teacher case-design time reduced by 63% (45 ± 12 vs. 122 ± 18 minutes, P \u0026lt; 0.001). Qualitative data revealed positive student experience, teacher recognition of AI question bank difficulty (with optimization needs), and suggestions for improvement plus concerns about AI answer accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: The AI-enabled PBL model enhances students’ academic performance, clinical learning and reasoning abilities, and teachers’ instruction quality. It serves as a practical example for medical education digital transformation and is worth wide promotion.\u003c/p\u003e","manuscriptTitle":"Generative AI-Assisted Construction of a PBL Case Library for Acute and Critical Care in the Emergency Department and Its Evaluation in Clinical Teaching","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 23:58:37","doi":"10.21203/rs.3.rs-7893461/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21f18145-7491-41d1-985d-2652b7cd7027","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T10:39:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 23:58:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7893461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7893461","identity":"rs-7893461","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}