## 2 Related Works  
Serious games have emerged as a promising tool across a range of domains, including therapy, education, and cognitive training. They have shown particular promise in supporting individuals with neurodevelopmental and mental health disorders [2]. Among the most studied populations are children with ASD and ADHD, for whom interactive, game-based methods can support cognitive, emotional, and behavioral interventions. For individuals with ASD, serious games have shown effectiveness in teaching social interactions and emotional understanding [3]. These games facilitate skill acquisition in diverse contexts but are frequently tailored to high-functioning individuals, limiting their accessibility [4]. Moreover, their clinical validation often falls short of rigorous medical standards [3], and game design practices are inconsistently reported, hindering replication and broader applicability [5]. Despite these limitations, the incorporation of structured game design elements and emotional training mechanics continues to expand [6]. In the ADHD domain, computer-assisted learning and gamified interventions have been used to enhance executive function [7]. Brain-computer interfaces (BCIs), combining EEG-based attention monitoring and feedback mechanisms, have been used both for treatment and as interaction tools in serious games [8,9]. Neurofeedback integrated into game-based training has shown efficacy across most behavioral symptoms, although attention deficits persist as a challenge [10]. VR-based emotion-recognition systems have improved emotion regulation in autistic children, yet scalability for differing severity levels remains an issue [11].  
Recent advances in HCI and AI-driven game design further demonstrate the potential of neuro-adaptive interventions. These include real-time EEG-based concentration monitoring for personalized game adaptation [12], as well as hybrid approaches that fuse eye-tracking and EEG to improve attention classification in BCIs [13]. Serious games have also been explored in low-resource settings through tangible interfaces [14], and machine learning applied to EEG data has enabled high-accuracy attention lapse prediction, albeit with limited interpretability [15]. Motion-based gaming interventions have proven to be effective in training impulse control in children with ADHD, although they may exclude those with motor impairments [16]. A recent systematic review identified common reward-based patterns in over 40 serious games but also emphasized the need for better personalization and longitudinal validation [17].  
Beyond the State-of-the-Art  
While these studies show significant progress in leveraging AI, neurofeedback, and multimodal HCI for therapeutic game-based interventions, they are often limited in termsof clinical scalability, individual adaptability, and interpretability of their models. In particular, the lack of explainability in AI decisions, the one-size-fits-all design approach, and the poor integration of multimodal data streams hinder real-world deployment. This work addresses some of these gaps by integrating facial expression analysis and EEG-based cognition assessment to adaptively select personalized stimuli for neurodivergent children. Our system contributes to the state-of-the-art by advancing real-time, AI-driven, context-aware interaction that is sensitive to each child's level of engagement.