# Empowering Children With Down Syndrome by Enhancing Emergency Preparedness Through Serious Games: Quasi-Experimental Study With a Between-Group Design

**Authors:** Samaa M Shohieb, Suzan Hassan Bakhit, Ensaf Mohammed, Abdelghafar M Elhady

PMC · DOI: 10.2196/73690 · JMIR Serious Games · 2025-10-17

## TL;DR

A serious game called Risk Resist improves emergency preparedness and engagement in children with Down syndrome compared to traditional methods.

## Contribution

The study introduces an adaptive serious game with dynamic difficulty adjustment for teaching emergency skills to children with Down syndrome.

## Key findings

- Children using the game-based approach showed significantly higher learning gains than those with traditional instruction.
- Engagement levels were significantly higher in the game-based group compared to the control group.
- A strong positive correlation was found between engagement and learning gains in the experimental group.

## Abstract

Children with Down syndrome (DS) often experience cognitive and adaptive challenges that affect their ability to acquire and retain critical life skills, including those needed for effective response during emergencies. Traditional training methods used to prepare children for crises are frequently static, noninteractive, and insufficiently tailored to the unique learning profiles of children with DS. These limitations contribute to reduced engagement, poor knowledge retention, and inadequate real-world preparedness. Recent advancements in game-based learning, particularly serious games, have demonstrated potential for enhancing education and skill development among individuals with cognitive impairments.

This study aimed to design, implement, and evaluate Risk Resist, an adaptive serious game developed to improve emergency preparedness in children with DS. The game incorporates a dynamic difficulty adjustment algorithm that personalizes the learning experience by dynamically modifying game difficulty based on real-time behavioral performance metrics. The study also assessed whether this adaptive game-based learning approach leads to superior learning gains and engagement compared to conventional teacher-led training.

A quasi-experimental, between-group design was used with 18 children diagnosed with DS, aged 8 to 12 years. Participants were randomly assigned to either an experimental group (n=9), which played Risk Resist, or a control group (n=9), which received traditional instruction on emergency scenarios. Learning outcomes were assessed using pre- and postintervention knowledge tests composed of 5 emergency-related questions. Engagement levels were measured through a structured 5-point Likert scale questionnaire completed by observing teachers. The game used a machine learning–driven dynamic difficulty adjustment model, specifically a Random Forest Regressor, which adjusted difficulty in response to individual performance indicators such as success rate, response time, and behavioral patterns during gameplay.

The experimental group achieved significantly higher learning gains (mean 3.6, SD 0.5) than the control group (mean 2.0, SD 0.5; P<.001). Engagement levels were also significantly greater in the game-based group (mean 4.54, SD 0.4) compared to the control group (mean 4.01, SD 0.32; P=.002). A strong positive correlation was identified between engagement and learning gain (r=0.85; P<.001), indicating that higher engagement contributed directly to improved knowledge acquisition.

The results support the effectiveness of Risk Resist in enhancing both engagement and learning outcomes for children with DS. The integration of adaptive difficulty algorithms provides a personalized, responsive learning experience, positioning serious games as a viable and impactful tool for emergency preparedness training in special education settings.

## Linked entities

- **Diseases:** Down syndrome (MONDO:0008608)

## Full-text entities

- **Diseases:** DS (MESH:D004314), cognitive impairments (MESH:D003072)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12579303/full.md

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12579303/full.md

## References

48 references — full list in the complete paper: https://tomesphere.com/paper/PMC12579303/full.md

---
Source: https://tomesphere.com/paper/PMC12579303