aceFaculty-supervised AI-supported BOPPPS-CBL and performance on a simulated neurological-emergency assessment among junior-college medical interns: a batch-stratified randomized educational study
Background County hospitals and township health centers in China provide frontline care for time-sensitive neurological emergencies, and three-year junior-college clinical medicine programs are an important pathway into this workforce. We evaluated a faculty-supervised artificial intelligence (AI)-supported BOPPPS (Bridge-in, Objectives, Pre-assessment, Participatory Learning, Post-assessment, and Summary) and case-based learning (CBL) instructional package among junior-college medical interns. Methods In this p…
The AI-supported group reported a greater perceived preparation-time burden, and the study design could not rule out cueing or carry-over from reused case contexts.
The study was not prospectively registered, used a locally developed assessment with reused case contexts, and does not establish transfer to workplace clinical performance or patient outcomes.
Evidence
- Peer-reviewedFrontiers in Medicine2026-10-14
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Truvace Impact Record TRV-2026-1472, v1: “Faculty-supervised AI-supported BOPPPS-CBL and performance on a simulated neurological-emergency assessment among junior-college medical interns: a batch-stratified randomized educational study.” Truvace, 2026-10-15. /record/TRV-2026-1472 (accessed at citation time). sha256 fa33237177e3826b…
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