This research proposal explores the methodological evolution of impact evaluation within Serious Games (SG) and gamified systems, focusing on the synthesis of disparate data streams to measure pedagogical and behavioral efficacy. A persistent challenge in Games User Research (GUR) is the “subjectivity gap” found in self-reported data, such as psychometric surveys and interviews, which, while essential for understanding conscious
motivation, are often susceptible to retrospective bias and social desirability. To address this, the project proposes a comparative analysis of peripheral indexes (including galvanic skin response, heart rate variability, and eye-tracking) against traditional surveys to identify points of convergence and divergence between physiological arousal and perceived experience. The research further distinguishes the evaluative requirements of entertainment-focused Video Games versus Serious Games, examining how the definition of effectiveness shifts when the objective is functional rather than purely hedonic. By leveraging AI to process the complexity of multi-modal data, this project aims to provide a robust, evidence-based toolkit that empowers researchers to validate the real-world impact of gamified interventions with unprecedented granularity and accuracy.