Adaptive Neurocognitive Reinforcement Learning for Personalized Autism Therapy: A Multi-Agent AI Framework Integrating Real-Time Behavioral Adaptation and Longitudinal Outcome Prediction

Abstract

This research introduces a novel Adaptive Neurocognitive Reinforcement Learning
(ANCRL) framework that represents a paradigm shift in autism therapy support systems.
Unlike previous machine learning applications focused primarily on detection or static
intervention recommendations, our framework dynamically adapts therapy strategies in
real-time based on individual neurocognitive responses, behavioral patterns, and longitudinal progress markers. The system employs a multi-agent architecture where specialized
AI agents collaboratively optimize therapy parameters, predict intervention effectiveness,
and prevent adverse outcomes through continuous learning. Our approach uniquely integrates neuroimaging biomarkers with behavioral data streams, creating personalized therapy pathways that evolve with the individual’s developmental trajectory. Through rigorous validation across multiple clinical sites involving 450 participants over 18 months,
we demonstrate a 42% improvement in therapy adherence and a 38% enhancement in developmental outcome prediction accuracy compared to current AI-assisted systems. This
research makes three distinctive contributions: (1) a novel multi-agent reinforcement learning system for therapy adaptation, (2) integration of real-time physiological and behavioral
feedback loops, and (3) a privacy-preserving federated learning implementation enabling
multi-institutional collaboration without data sharing.