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.
- 1Holistic Center for Neurological Diseases, The People's Hospital of Chongqing Yubei District, Chongqing, China.
- 2School of Clinical Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, China.
Abstract
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 prospective, batch-stratified randomized educational study, 118 interns from 12 monthly rotation batches were assigned to AI-supported BOPPPS-CBL (n = 58) or conventional BOPPPS-CBL (n = 60) during a one-month neurology rotation. The intervention used standardized DeepSeek prompts for Socratic questioning, error-focused feedback, and structured reflection under faculty supervision. The primary endpoint was the total score on a locally developed four-station simulated neurological-emergency assessment (0-100). Secondary outcomes included a written examination, while learner-reported questionnaire items were exploratory. Regression models adjusted for rotation batch, baseline academic performance, and digital literacy. The study was not prospectively registered; no public trial registration identifier or registry URL exists, and no publicly time-stamped protocol or statistical analysis plan was available.Results
All 118 participants completed the primary assessment. Mean total scores were 82.07 in the AI-supported group and 76.43 in the control group, with an adjusted between-group difference of 5.67 points (95% CI, 2.86-8.49; P < 0.001; adjusted Cohen's d = 0.74). In secondary station-specific analyses, only lesion localization met the multiplicity-adjusted threshold (adjusted mean difference, 2.58 points; 95% CI, 1.25-3.91; P < 0.001; adjusted d = 0.71). A Station 2-specific ICC was not estimated. Post hoc sensitivity analyses excluding the station containing reused case contexts yielded standardized estimates of similar magnitude but could not exclude cueing or carry-over. Written-examination estimates were smaller and imprecise. Exploratory learner-reported findings indicated greater perceived preparation-time burden in the AI-supported group.Conclusion
Assignment to the faculty-supervised AI-supported BOPPPS-CBL package was associated with higher scores on a locally developed simulated neurological-emergency assessment than conventional BOPPPS-CBL. These findings do not establish transfer to workplace clinical performance or patient outcomes. Multicenter studies using independent assessment cases, longer-term follow-up, and workplace-based outcomes are warranted.
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BibTeX
@article{zhang2026facultysupervised,
title = {{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.}},
author = {Junhuai Zhang and Zhanqin Xiao and Xiang Gu and Qi Lang and Chanxi Chen and Yaxi Chen},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1945397},
url = {https://europepmc.org/article/MED/42952540},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Junhuai Zhang
AU - Zhanqin Xiao
AU - Xiang Gu
AU - Qi Lang
AU - Chanxi Chen
AU - Yaxi Chen
PY - 2026
DA - 2026/10/14
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1945397
UR - https://europepmc.org/article/MED/42952540
AB - 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 prospective, batch-stratified randomized educational study, 118 interns from 12 monthly rotation batches were assigned to AI-supported BOPPPS-CBL (n = 58) or conventional BOPPPS-CBL (n = 60) during a one-month neurology rotation. The intervention used standardized DeepSeek prompts for Socratic questioning, error-focused feedback, and structured reflection under faculty supervision. The primary endpoint was the total score on a locally developed four-station simulated neurological-emergency assessment (0-100). Secondary outcomes included a written examination, while learner-reported questionnaire items were exploratory. Regression models adjusted for rotation batch, baseline academic performance, and digital literacy. The study was not prospectively registered; no public trial registration identifier or registry URL exists, and no publicly time-stamped protocol or statistical analysis plan was available.Results All 118 participants completed the primary assessment. Mean total scores were 82.07 in the AI-supported group and 76.43 in the control group, with an adjusted between-group difference of 5.67 points (95% CI, 2.86-8.49; P < 0.001; adjusted Cohen's d = 0.74). In secondary station-specific analyses, only lesion localization met the multiplicity-adjusted threshold (adjusted mean difference, 2.58 points; 95% CI, 1.25-3.91; P < 0.001; adjusted d = 0.71). A Station 2-specific ICC was not estimated. Post hoc sensitivity analyses excluding the station containing reused case contexts yielded standardized estimates of similar magnitude but could not exclude cueing or carry-over. Written-examination estimates were smaller and imprecise. Exploratory learner-reported findings indicated greater perceived preparation-time burden in the AI-supported group.Conclusion Assignment to the faculty-supervised AI-supported BOPPPS-CBL package was associated with higher scores on a locally developed simulated neurological-emergency assessment than conventional BOPPPS-CBL. These findings do not establish transfer to workplace clinical performance or patient outcomes. Multicenter studies using independent assessment cases, longer-term follow-up, and workplace-based outcomes are warranted.
SN - 2296-858X
ER -