Designing AI-Enhanced Educational Games for Cognitive Skill Development
Annotatsiya
This article describes a novel adaptive educational game concept that leverages AI to give youngsters individualized, real-time chances to learn new cognitive skills. The suggested three-stage approach adjusts content difficulty and delivery based on learner reaction, physiological participation, and cognitive growth. Real-time analytics, physiological feedback, prediction modelling, and smart material selection keep students in the "zone of proximal development," challenging them without frustrating or losing interest. In the first stage, baseline performance indicators and biometric data are evaluated to create an engagement profile. To ensure smooth dynamic changes to challenge, these data points are confined, normalized, and rate-limited. The learner's shifting cognitive and emotional state is used to select educational material in the second stage utilizing weighted scoring and softmax-based probabilistic methods. Recent interaction data is used to adjust micro-level difficulty in the final stage. This enhances customization and learning. Twelve experimental performance criteria reveal that the recommended strategy outperforms standard and semi-adaptive educational games. Engagement, flexibility, response accuracy, customization, and learning gain all improved significantly. The system can provide real mental stimulation and long-term motivation. Scores for emotional recognition and real-world transferability illustrate the method's value outside of games. This smart, flexible framework may alter cognitive skill instruction in various schools by providing scalable, tailored, and data-driven learning interventions.