Volume 1, Issue 2

Surgery of Traditional Chinese Medicine (TCM) is a core course within the TCM curriculum and an indispensable clinical discipline for all medical students transitioning to professional practice. With the deepening of curriculum reforms, the integrated teaching model has proven effective in helping students master basic theories and clinical skills. Among various teaching models, the Bridge-in, Learning Objectives, Pre-Assessment, Participatory Learning, Post-Assessment, and Summary (BOPPPS) model combined with Problem-Based Learning has gained widespread recognition and application. However, its application in TCM surgery education remains limited. This paper integrates the BOPPPS and problem-based learning (PBL) teaching models into the TCM surgery classroom, using eczema as a case study. This teaching design encompasses six elements: bridge-in, learning objectives, pre-assessment, participatory learning based on Problem-Based Learning, post-assessment, and summary. Potential challenges during the teaching process are also examined to enhance students’ clinical critical thinking abilities, improve the quality of classroom teaching, and further cultivate high-quality TCM professionals through the application of the BOPPPS-PBL model.

This study addresses the limitations of traditional disaster medicine course assessments, including single evaluation formats, delayed feedback mechanisms, and gaps in competency mapping, by developing a diversified assessment system leveraging the Rain Classroom platform. The system incorporates six interconnected evaluation components across the learning cycle: pre-class preparation, pre-class tests, case discussions, skills assessment, post-class tests, and post-class feedback, collectively forming a three-dimensional “cognitive-skill-attitude” assessment framework. In the assessment design, the weighting of practical skill evaluation is elevated to 40% to prioritize the development of students’ disaster response competencies. Additionally, an innovative multi-subject evaluation model (“self–peer–teacher”) is implemented within disaster scenario simulations, utilizing standardized scoring rubrics. This methodology not only enables comprehensive performance evaluation but also fosters critical teamwork and reflective practice. Implementation outcomes demonstrated that the system effectively evaluates learning progress through multi-modal assessments, enhances disaster rescue knowledge and skill proficiency, and successfully achieves predefined pedagogical objectives.

Artificial Intelligence is profoundly transforming innovation and development in healthcare and education. In this study, we developed an AI-empowered blended learning model for disaster medicine. Leveraging the Rain Classroom platform, we established a comprehensive intelligent teaching support system covering the entire learning cycle—pre-class, in-class, and post-class. Through AI-driven enhancements, the model enables intelligent resource allocation, personalized learning paths, and high-fidelity simulation of practical training scenarios. Moreover, it addresses key challenges in traditional disaster medicine education, including fragmented knowledge delivery, insufficient practical training environments, and limited evaluation methods. Ultimately, the model enhances both the efficiency and effectiveness of disaster medicine education.

Objectives: To evaluate the effectiveness of an innovative teaching framework combining sandwich methodology and microteaching in improving emergency skill training outcomes among resident physicians. Methods: A randomized controlled trial was conducted involving 92 residents enrolled in standardized training programs. Participants were randomly allocated into two groups: the Experimental Group (EG, n=46), which received training via the sandwich-microteaching method in a simulated ICU, and the Control Group (CG, n=46), which received conventional teaching. Both groups underwent identical core curriculum content. Data collected included demographics (gender, age, resident year), theoretical knowledge scores, practical skill performance scores, self-assessed mastery levels, and course satisfaction. Results: Baseline characteristics showed no significant differences between groups (gender p=0.527, age p=0.394, resident year p=0.661). The EG demonstrated significantly higher theoretical scores (94.80±1.54 vs. 92.70±3.48, p<0.001) and practical skill scores (93.65±3.06 vs. 89.20±4.74, p<0.001) compared to the CG. Satisfaction rates were markedly elevated in the EG (95.65% vs. 78.26%, p=0.030). While overall self-assessed mastery distributions were similar (p=0.193), the EG reported a higher proportion of expert-level mastery (self-assessed level 10). Conclusion: This innovative teaching framework significantly improves emergency skill proficiency and learner satisfaction, while fostering clinically meaningful improvements in self-perceived expertise. The combined sandwich-microteaching approach represents a promising strategy for high-quality emergency skill training in residency programs.

Basic anesthesiology is a core course in anesthesiology education, and it is crucial to improve its teaching effectiveness. This proposal focuses on optimizing the lesson preparation and teaching methods for basic anesthesiology based on competency. A comprehensive anesthesiologist competency model is established, covering clinical knowledge, crisis management, doctor-patient communication, and research innovation. The course objectives and content are restructured to integrate core knowledge with competency elements. In terms of lesson preparation, a layered teaching design is adopted, incorporating real case libraries and ideological and political elements. The teaching approach adopts a blended teaching mode, utilizing diverse methods. Additionally, the teaching evaluation system is also reformed based on Miller's Pyramid. This optimized model aims to enhance students' clinical thinking, operational skills, and professional qualities, while reducing the job adaptation period.

Objective: The anesthesia residency training system is designed to provide supervised practice, enabling residents to progress from simple to complex procedures and higher-risk patients. However, it remains unclear whether residents acquire sufficient competence to be considered qualified anesthesiologists by the end of their training. This study aimed to evaluate whether anesthesia care provided by supervised CA-5 residents affects postoperative outcomes in elderly patients undergoing non-cardiac surgery. Methods: A retrospective analysis was conducted on clinical data from elderly patients who underwent non-cardiac surgery between January 2020 and December 2021 at Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Patients were categorized into two groups: those managed by CA-5 residents (Resident group, n=294) and those managed by attending anesthesiologists (Attending group, n=521). Propensity score matching (PSM; 1:1) was used to ensure comparability between the groups. The primary outcome was a composite of in-hospital postoperative complications. Secondary outcomes included intraoperative hemodynamic changes, the need for intensive care unit (ICU) admission, length of ICU and hospital stays, and in-hospital mortality. Multivariable logistic regression assessed the adjusted association between anesthesia provider type and postoperative morbidity and mortality. Results: Among the 815 elderly patients included, 105 (12.9%) experienced postoperative complications and 22 (2.7%) died during hospitalization. No significant differences were observed in postoperative complications or mortality between the two groups, either before PSM (morbidity: 11.9% vs. 13.4%, p=0.531; mortality: 3.7% vs. 2.1%, p=0.168) or after PSM (morbidity: 12.0% vs. 14.4%, p=0.392; mortality: 3.8% vs. 1.4%, p=0.067). Multivariate analysis confirmed that postoperative morbidity and mortality were not significantly associated with resident involvement, either before PSM (morbidity: OR=0.882, 95% CI: 0.552-1.410, p=0.600; mortality: OR=1.293, 95% CI: 0.479-3.492, p=0.612) or after PSM (morbidity: OR=0.881, 95% CI: 0.523-1.486, p=0.636; mortality: OR=3.122, 95% CI: 0.805-12.106, p=0.100). Conclusions: Postoperative morbidity and mortality rates in elderly patients undergoing non-cardiac surgery are comparable between those anesthetized by supervised CA-5 residents and those managed by attending anesthesiologists. These results suggest that supervised CA-5 residents do not adversely affect patient safety.

Exploring new innovative approaches and models for medical school class advisors to participate in student management is essential under the comprehensive promotion of moral education and talent cultivation. Taking the "Five-Dimensional Education" model as an example, the School of Anesthesiology of Wannan Medical College redefines the roles of class advisors as builders of class ecology, leaders of value creation, companions on the growth journey, practitioners of lifelong learning, and connectors of human efforts, forming a comprehensive and multi-dimensional framework for student education management. This model effectively enhances the quality of talent cultivation in anesthesiology and optimizes the efficiency of educational management. By implementing effective assessment mechanisms, it ensures that class advisors can perform ideological and political education and academic guidance in an efficient, high-quality, and orderly manner. This study not only helps to cultivate medical talents with both moral integrity and professional competence, but also provides valuable theoretical and practical references for reforming student management in medical institutions, thereby promoting the sustainable development of medical education.

Generative artificial intelligence (Generative AI) is reshaping both learning and teaching paradigms in medical education. With the advancement of Large Language Models (LLMs)-based tools such as ChatGPT, Gemini, and other medical-domain-specific models, Generative AI shows strong potential to address persistent challenges in medical education, including rigid curricula, unequal access to educational resources, and the diverse learning needs of medical students. This review summarizes the applications of Generative AI across key domains: (1) personalized learning through real-time analysis of student performance; (2) clinical skills training via immersive simulations and virtual patients; (3) automated generation of teaching materials such as clinical cases and assessments; and  (4) support for student research and academic writing. Empirical evidence indicates that Generative AI-enhanced instruction can improve knowledge acquisition, clinical reasoning, and overall educational efficiency. However, challenges remain, including the generation of inaccurate or fabricated content, risks to academic integrity, algorithmic bias, data privacy concerns, and unresolved ethical issues regarding AI's role in teaching. Without proper oversight, these risks may compromise educational quality and equity. To ensure responsible adoption, this review advocates for the establishment of institutional policies, enhancement of educators' AI literacy, transparent model validation, and a human-centered design framework that positions Generative AI as a collaborative teaching assistant. When responsibly integrated, Generative AI holds the transformative potential to cultivate future medical professionals equipped with clinical competence, responsibility, and innovative thinking.