Serious games-games that have additional purposes rather than only entertainment-aim to educate people, solve, and plan several real-life tasks and circumstances in an interactive, efficient, and user-friendly way. Emergency training and planning provide structured curricula, rule-based action items, and interdisciplinary collaborative entities to imitate and teach real-life tasks. This rule-based structure enables the curricula to be transferred into other systematic learning platforms. Although emergency training includes these highly structured and repetitive action responses, a general framework to map the training scenarios' actions, roles, and collaborative structures to serious games' game mechanics and game dialogues, is still not available. To address this issue, in this study, a scenario-based game generator, which maps domain-oriented tasks to game rules and game mechanics, was developed. Also, two serious games (i.e., Hospital game and BioGarden game) addressing the training mechanisms of Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNe) domain, were developed by both the game developers and the scenario-based game generator for comparative analysis. Finally, the outcomes of these games were mapped to the virtual reality environment to provide a thorough training program. To test the usability, immersion, presence, and technology acceptance aspects of the proposed game generator's outcomes, 15 game developer participants tested a complete set of games and answered the questionnaires of the corresponding phenomenon. The results show that although the game generator has higher CPU time and memory usage, it highly outperforms the game development pipeline performance of the game developers and provides usable and immersive games. Thus, this study provides a promising game generator which bridges the CBRNe practitioners and game developers to transform real-life training scenarios into video games efficiently and quickly. KeywordsSerious games • Video game generator • CBRNe • System usability scale • Technology acceptance model This framework is fully supported by European Network Of CBRN TraIning Centers (eNOTICE) project funded under EU H2020 (Project ID: 740521).
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