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Enhancing Emergency Medical Response Education through Generative AI-Powered Game-Based Learning: A Retrospective

Yaning Lai¹, Xiaoqin Lai², Hai Hu³

¹Academic Affairs Department, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.

Prehospital and Disaster Medicine

|July 16, 2026

Summary

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Generative AI-powered game-based learning (AI-GBL) enhances medical students' knowledge acquisition and retention in Emergency Medical Response (EMR) training. This AI-GBL approach also reduces cognitive load compared to traditional methods.

Area of Science:

Medical Education
Artificial Intelligence in Education
Emergency Medical Response Training

Background:

Traditional lecture-based learning (LBL) is inadequate for developing critical decision-making skills in high-stakes fields like Emergency Medical Response (EMR).
Game-based learning (GBL) offers immersive training but often lacks real-time expert feedback.
Integrating generative Artificial Intelligence (AI) as an intelligent tutor within GBL addresses this gap.

Purpose of the Study:

To compare the effectiveness of LBL, GBL, and AI-GBL on medical students' knowledge acquisition and retention.
To evaluate the impact of these learning modalities on student motivation and cognitive load.
To assess the utility of AI-GBL in an EMR context.

Main Methods:

A retrospective comparative study involved 86 medical students across three cohorts (2022-2024).

Keywords:

Disaster Medicine Gamification Generative Artificial Intelligence Medical Education Medical Student

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Each cohort experienced one learning modality: LBL (n=29), GBL (n=28), or AI-GBL (n=29).

Knowledge was measured using pre-test, post-test, and final-test scores; student feedback assessed motivation, cognitive load, and technology acceptance.

Main Results:

Both GBL and AI-GBL significantly improved immediate knowledge acquisition compared to LBL.
AI-GBL demonstrated superior delayed knowledge retention over both GBL and LBL.
AI-GBL resulted in significantly lower cognitive load and was perceived as more useful than GBL.

Conclusions:

AI-enhanced GBL is a promising model for EMR training, significantly improving knowledge acquisition and retention.
This approach effectively reduces cognitive load, enhancing learning efficiency.
AI-GBL shows potential for developing complex medical competencies in high-stakes environments.