Citation: G. Liapis and I. Vlahavas, "Personality Assessment System Using Artificial Intelligence in a Game Environment," in IEEE Transactions on Games, vol. 17, no. 4, pp. 1098-1111, Dec. 2025, doi: 10.1109/TG.2025.3605750.

Personality traits are essential for understanding human behavior, with applications in many areas, such as job screening and team building. Traditional self-assessment methods are prone to biases and often mundane, leading to misleading results. To address these limitations, we propose MindEscape a novel game-based approach using a digital 3-D Escape Room (ER), and artificial intelligence-driven models, based on the Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN) five personality traits. We developed a multioutput regression system, which is used to make personality assessment to human players based on their gameplay data. As this Articial Intelligence (AI) model needs labeled data to be trained on, we developed single-agent and multi agent systems that emulate human behaviors using custom reward functions powered by Deep Reinforcement Learning (RL) and generate the necessary data to train the system. Preliminary experiments with students from Greece and Italy show strong correlations between game-derived profiles and questionnaire baselines. This methodology offers an engaging, scalable, and objective alternative for individual and team personality evaluation, advancing data-driven assessment methods.

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