Gamification for Wildfire Education and Safety Training: A Systematic Literature Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background Wildfires have become increasingly frequent and destructive, highlighting the need for more effective public education on safety and preparedness. Gamification, the use of game design elements in non‐game contexts, offers a promising strategy to enhance learner engagement and educational effectiveness compared to traditional methods. Objective This study aims to investigate the application of gamification in wildfire education and training, evaluating its effectiveness and highlighting key benefits and challenges. Methods A systematic literature review was conducted using the PRISMA 2020 framework. The review includes 38 articles selected from the Web of Science (WoS) and Scopus databases, which were published from 2007 to 2025, pertinent to the integration of gamification in wildfire simulation or education applications. This review examined gamification in wildfire education through planning, conducting and reporting stages, and included a meta‐analysis to assess the effect size of immersive versus non‐immersive applications. Eligible studies were quality assessed using predefined criteria and analysed to extract key characteristics. VOSviewer was used to conduct a keyword co‐occurrence analysis, identifying major research themes. SPSS was used to calculate the effect size for the meta‐analysis. Results and Conclusions The findings reveal that different gamification strategies distinctly influence user engagement, motivation, learning effectiveness and overall user experience within wildfire education contexts. Through keyword co‐occurrence analysis, the study maps the intellectual landscape of the field, identifying key thematic clusters and emerging trends. Moreover, the meta‐analysis provides empirical evidence of the impact of immersive gamification, showing a small but statistically significant effect in learning outcomes (Hedges' g = 0.18, p = 0.04). This review identifies five critical research gaps: the underrepresentation of safety behaviour outcomes, limited theoretical integration, lack of community‐level and prevention‐oriented educational interventions and insufficient attention to implementation barriers. These insights offer a targeted research agenda and practical guidance for advancing the design and deployment of gamified wildfire education initiatives. The novelty and contribution of this study lie in the comprehensive synthesis on the functional roles of gamification in shaping learning outcomes in the wildfire education context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".