## 1 Introduction  
Neurodiversity presents significant challenges in both healthcare and education. Globally, approximately 1 in 100 children (ranging from 1 in 36 in countries such as the USA, UK, and Brazil) is diagnosed with ASD. Additionally, ADHD affects around 5-7% of children and 2.5% of adults worldwide, making it one of the most prevalent neurodevelopmental conditions. Beyond these, 1 in 8 individuals (around 970 million people) globally live with a mental disorder, with anxiety and depressive disorders being the most common, according to the World Health Organization (WHO) [1].  
Despite the increasing demand for scalable and adaptive interventions to support cognitive and emotional well-being, traditional Human-Computer Interaction (HCI) systems predominantly rely on unimodal approaches that lack real-time adaptation. NAI offers an innovative solution by enabling adaptive systems that respond dynamically to cognitive and emotional states. Unlike conventional affective computing, NAI integrates affective and cognitive processes, allowing AI to modulate interactions based on neuro-affective regulation, which is critical for decision-making, attention modulation, and emotional self-regulation. To address these limitations, we propose an NAI system for adaptive Child-Game Interaction (CGI), as illustrated in Figure 1, integrating real-time EEG signals, facial emotion analysis, and performance metrics to monitor and respond to engagement levels. A central innovation of this work is the Bayesian Immediate Feedback Learning (BIFL) framework, which optimizes stimulus delivery by continuously updating its belief model using Bayesian inference and multi-armed bandit theory. The system dynamically selects and delivers the most effective modality (visual, auditory, or textual) based on individual neuro-affective feedback during gameplay. Unlike prior systems, our approach enables personalized real-time adaptation without requiring long training or predefined rules. The engagement score is calculated through a dynamic weighting model, assigning importance to each modality based on reliability, measured by an uncertainty measure. This ensures that the multimodal engagement score merges the multisensory information (e.g., concentration, emotion, and game score) based on the most stable and informative input source over time.  
The key contributions of this work are as follows:  
* A novel Bayesian Immediate Feedback Learning (BIFL) framework that adaptively selects the most appropriate stimulus modality (visual, auditory, or text) throughout gameplay, based on real-time multimodal feedback from EEG concentration, facial expressions, and game performance. The selected stimulus is that which has im  
Figure 1: The proposed system integrates multisensory biophysical data to assess neuro-affective states in real time. Adaptive interventions are delivered through a game interface, generating multiple stimuli to improve engagement.  
proved engagement and supported emotional regulation in neurodivergent children by analyzing their biophysical responses.
* A robust multimodal engagement computation model with dynamic weighting based on signal stability.
* Experimental validation with 40 neurodivergent children over four weeks, showing significant improvements in concentration, emotional engagement, and task performance.
* Statistical evaluation demonstrating high effect sizes and significant improvements, validating the system's effectiveness.  
This study is part of the NeuroEngage Project (2020-2024), supported by UKRI, CONFAP, and IEEE RAS-SIGHT, and it aims to advance scalable AI solutions for therapeutic cognitive training in neurodiverse populations.  
The remainder of this paper is structured as follows: Section 2 reviews related work, Section 3 details the methods, Section 4 describes the experimental design, Section 5 presents the results, and Section 6 concludes with future directions.