REVIEW OF THE USE OF AI TECHNIQUES IN SERIOUS GAMES: DECISION MAKING AND MACHINE LEARNING

Abstract

Serious games have emerged as powerful tools for education, training, and skill development by combining entertainment with practical learning objectives. The integration of Artificial Intelligence (AI) techniques has significantly enhanced the effectiveness of serious games, particularly in the areas of decision making and adaptive learning. This paper reviews the application of AI methods such as machine learning, reinforcement learning, and intelligent agents in serious gaming environments. These techniques enable dynamic content generation, real-time feedback, and personalized user experiences by analyzing player behavior and performance. AI-driven decision-making models allow serious games to simulate complex real-world scenarios, helping users develop critical thinking and problem-solving skills in a controlled environment. Machine learning algorithms further enhance these systems by continuously learning from user interactions and adapting game difficulty accordingly. The review highlights key advancements, including player modeling, strategy optimization, and autonomous agent behavior. However, challenges such as high computational requirements, data dependency, and ethical concerns related to user data are also discussed. Overall, the study emphasizes the growing importance of AI in serious games and its potential to revolutionize training and education systems by providing intelligent, immersive, and personalized learning experiences.