An Extended Study with LLM-Generated Synthetic Data of ArSGam: A Framework for Cognitive-Affective Personalization and Gamification in Serious Game
DOI:
https://doi.org/10.19153/cleiej.29.3.4Keywords:
Software Architecture, Gamification, Serious Games, Cognitive-Affective ModelAbstract
This article presents an in-depth analysis of ArSGam, a modular architecture designed to integrate gamification strategies into serious games for cognitive-affective training. The original work introduced four interconnected modules that guide the process from user profiling to the integration of game mechanics. This extended version strengthens the scientific contribution by conducting additional experiments with ArSGam and employing Large Language Models (LLMs ) as tools for generating synthetic data to expand testing scenarios. Furthermore, new case studies and validation analyses were performed to examine the architecture's operational consistency and applicability in high-risk training contexts. A comparison with related architectures is also provided to contextualize the distinctive features of ArSGam. The results reaffirm the relevance of incorporating cognitive-affective profiling into gamification design and highlight the flexibility of the architecture for supporting personalized serious game experiences. Consequently, this study consolidates ArSGam as a robust and versatile framework for integrating cognitive-affective personalization and gamification within serious games.
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