Research Article

Open Access

2025 Sep;1(2):77-84

https://doi.org/10.61189/793750gpxnge

An AI-empowered blended learning model for disaster medicine education

Linlin Chen

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zhibin Wang

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Xiaojing Guo

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zhanheng Chen

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zixin Li

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Mi Li

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Weiheng Xu

School of Pharmacy, Naval Medical University, Shanghai 200433, China.

Zui Zou

zouzui1980@163.com

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Shuo Yang

charlotteyang@smmu.edu.cn

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Address correspondence to

Article notes

Highlights

Shuo Yang, Department of Critical Care Medicine, School of Anesthesiology, Naval Medical University, 168 Changhai Road, Shanghai 200433, China. E-mail: charlotteyang@smmu.edu.cn; Zui Zou, School of Anesthesiology, Naval Medical University, 168 Changhai Road, Shanghai 200433, China. E-mail: zouzui1980@163.com.

Received June 23, 2025; Accepted August 28, 2025; Published September 30, 2025

This study introduces an AI-empowered blended teaching model for disaster medicine, integrating generative AI, virtual simulations, and intelligent assessment systems to improve teaching efficiency and student engagement.
The model employs a “dual-teacher collaboration” approach, combining AI-driven tools with human instructors to foster critical thinking and ethical awareness in disaster response training.
A multidimensional evaluation system was developed, combining dynamic AI-based assessments, scenario- driven simulations, and long-term tracking to provide personalized feedback and support continuous learning.
The course emphasizes interdisciplinary integration across engineering, information technology, and psychology, aiming to cultivate comprehensive disaster management competence and strengthen professional responsibility.

Previous Article Next Article

Research Article

Open Access

An AI-empowered blended learning model for disaster medicine education

Linlin Chen

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zhibin Wang

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Xiaojing Guo

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zhanheng Chen

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Zixin Li

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Mi Li

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Weiheng Xu

School of Pharmacy, Naval Medical University, Shanghai 200433, China.

Zui Zou

zouzui1980@163.com

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Shuo Yang

charlotteyang@smmu.edu.cn

School of Anesthesiology, Naval Medical University, Shanghai 200433, China.

Address correspondence to

Article notes

Received June 23, 2025; Accepted August 28, 2025; Published September 30, 2025

Highlights

This study introduces an AI-empowered blended teaching model for disaster medicine, integrating generative AI, virtual simulations, and intelligent assessment systems to improve teaching efficiency and student engagement.
The model employs a “dual-teacher collaboration” approach, combining AI-driven tools with human instructors to foster critical thinking and ethical awareness in disaster response training.
A multidimensional evaluation system was developed, combining dynamic AI-based assessments, scenario- driven simulations, and long-term tracking to provide personalized feedback and support continuous learning.
The course emphasizes interdisciplinary integration across engineering, information technology, and psychology, aiming to cultivate comprehensive disaster management competence and strengthen professional responsibility.

2025 Sep;1(2):77-84

doi:https://doi.org/10.61189/793750gpxnge

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Abstract

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.

Keywords: Disaster medicine, blended learning, rain classroom, artificial intelligence, multidimensional assessment

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ISSN: 3007-0007

Volume 1, Issue 2

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Pages: 63-123

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