An AI-Based Adaptive Learning Framework and Algorithm with Gesture Interaction and Behavior-Mood Aware Adaptation for IDD Children

The settings that children with Intellectual and Developmental Disabilities (IDD) need are interactive, emotionally receptive and personally adaptive. Traditional e-learning products are not very flexible and they are based on common input features that inhibit interaction and customization to learners with IDD. The current paper introduces an adaptive learning system and algorithm powered by AI which that combines gesture-based interaction, learning games, analyzing behaviors and recognition of moods based on interaction to provide individualized learning experiences. The suggested system allows children with IDD to play with the educational games and learning materials with the help of natural hand movements. The interaction indicators used to monitor the behavior of learners are response time, gesture repetition, inactivity time, and patterns of task completion. It uses an Interaction-Based Mood Recognition (IMR) mechanism which recognizes the emotional states of a learner such as engaged, neutral, frustrated and fatigued without any intrusive sensors or biometric measurements. According to the results of the joint analysis of behavioral tendencies and moods, the adaptive algorithm dynamically changes the content of learning, the complexity of a game, the speed of instructions, and the level of guidance. As opposed to the current adaptive learning systems, which mostly use performance scores, the proposed solution focuses on emotional comfort, long-term participation, and adaptability in learners. The system architecture, framework design, adaptive algorithm and methodology of evaluation are outlined. Parameters that are observed in order to conduct experimental evaluation are engagement stability, interaction accuracy, and mood-sensitive adaptation.