Our IEEE Transactions on Games paper, “Personality Assessment System Using Artificial Intelligence in a Game Environment,” brings together several years of research on digital escape rooms, personality modelling, artificial intelligence, and behavioural analysis.
The paper presents the scientific and technical foundations of MindEscape—SeriousMind’s flagship serious game for personality assessment.
Rather than treating questionnaires and games as separate tools, the system integrates assessment directly into gameplay. The player enters a digital 3D escape room, solves challenges, makes decisions, and responds to pressure. The game becomes the assessment environment, while AI analyses the resulting behaviour.
Traditional personality assessment usually asks people to describe themselves through questionnaires.
MindEscape takes a different approach: it observes how people act while they play.
Players must explore environments, solve puzzles, manage their time, decide what to do next, and respond to changing conditions. These actions produce behavioural data that can be connected to the Big Five personality model, also known as OCEAN:
The objective is not to replace established psychological methods with a game. It is to explore how gameplay can provide an additional behavioural channel for personality assessment.
The paper describes a complete system architecture that connects several components:
This integration is important because the assessment does not happen after the game as a separate questionnaire. It happens through the game itself.
The player’s exploration, puzzle-solving process, decision-making, timing, and reactions under pressure all contribute to the assessment experience.
A key part of the research involves deep reinforcement learning (DRL) and personality emulation.
The system uses AI agents that can learn to behave according to different personality profiles within game environments. These agents generate diverse behavioural patterns and help researchers study how personality-related behaviours may influence decisions, task performance, and collaboration.
This approach can help address a major challenge in game-based personality research: collecting large and diverse datasets from real participants with known personality profiles.
Research on reinforcement-learning-based personality emulation describes how OCEAN-aligned reward functions can encourage agents to produce trait-related behaviour and generate data for studying individual and team performance.agris.fao+1
The agents are not intended to replace human participants. They function as research tools that support modelling, simulation, testing, and system development.
The assessment system does not treat every action as a direct measurement of one personality trait.
Instead, it analyses combinations of behavioural signals. For example, the system may consider:
These metrics are processed by multi-output models that estimate multiple personality dimensions together.
This is important because human behaviour is complex. A single action may reflect personality, strategy, prior gaming experience, mood, or the demands of the situation. A reliable system must therefore analyse patterns across multiple tasks and contexts rather than draw conclusions from one isolated choice.
The paper forms an important academic foundation for SeriousMind’s products.
It connects laboratory research with practical applications in:
MindEscape translates this research into an accessible experience for individuals and organisations. Players can engage with the game as a journey of self-discovery, while organisations can use it as a complementary source of behavioural insight.
The research has also received recognition through the IEEE Transactions on Games Computer Intelligence and Serious Games highlight programme, which described MindEscape as a game-based approach using 3D escape rooms and AI-driven models based on the OCEAN personality traits.xplorestaging.ieee+1
The value of MindEscape lies in the combination of immersion, behavioural data, and scientific modelling.
A questionnaire asks people to report how they usually behave. MindEscape creates situations in which players demonstrate how they approach challenges. These two methods do not have to compete. They can complement one another.
A responsible assessment process should combine multiple sources of evidence, communicate limitations clearly, and ensure that results are interpreted in context.
MindEscape is therefore not an automatic authority on personality. It is a research-grounded tool designed to provide additional insight through structured gameplay.
The journey from an IEEE research paper to a commercial serious game illustrates how academic research can become practical innovation.
The research contributed the models, system architecture, and behavioural framework. Product development transformed those ideas into an interactive experience that users can play on digital platforms.
For SeriousMind, this is the connection between:
The larger vision is to make personality assessment more engaging, evidence-based, and connected to behaviour.
In MindEscape, gameplay is the test, AI is the analytical engine, and the player’s actions become the data.
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