Adaptive Neuro-Affective Engagement via Bayesian Feedback Learning in Serious Games for Neurodivergent Children

Neuro-Affective Intelligence (NAI) integrates neuroscience, psychology, and artificial intelligence to support neurodivergent children through personalized Child–Machine Interaction (CMI). This paper presents an adaptive neuro-affective system designed to enhance engagement in children with neurodevelopmental disorders through serious games. The proposed framework incorporates real-time biophysical signals—including EEG-based concentration, facial expressions, and in-game performance—to compute a personalized engagement score. We introduce a novel mechanism, Bayesian Immediate Feedback Learning (BIFL), which dynamically selects visual, auditory, or textual stimuli based on real-time neuro-affective feedback. A multimodal CNN-based classifier detects mental states, while a probabilistic ensemble merges affective state classifications derived from facial expressions. A multimodal weighted engagement function continuously updates stimulus–response expectations. The system adapts in real time by selecting the most appropriate cue to support the child’s cognitive and emotional state. Experimental validation with 40 children (ages 6–10) diagnosed with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) demonstrates the system’s effectiveness in sustaining attention, improving emotional regulation, and increasing overall game engagement. The proposed framework—combining neuro-affective state recognition, multimodal engagement scoring, and BIFL—significantly improved cognitive and emotional outcomes: concentration increased by 22.4%, emotional engagement by 24.8%, and game performance by 32.1%. Statistical analysis confirmed the significance of these improvements (p<0.001, Cohen’s d>1.4). These findings demonstrate the feasibility and impact of probabilistic, multimodal, and neuro-adaptive AI systems in therapeutic and educational applications.