Towards a Supporting Framework for Neuro-Developmental Disorder: Considering Artificial Intelligence, Serious Games and Eye Tracking
Abdul Rehman, Ilona Heldal, Diana Stilwell, Jerry Chun-Wei Lin
TL;DR
This work proposes a framework that integrates artificial intelligence, serious games, and eye-tracking to study neurodevelopmental disorders (NDD) in children, uncovering performance signals such as gaze patterns, sustained attention, and stimulus expectancy to inform teachers and clinicians. It defines three objectives: preprocess data to yield actionable insights (Obj1), predict patterns that indicate how games can improve well-being and performance (Obj2), and provide explainable, privacy-preserving decisions (Obj3) via a federated deep-learning approach. Drawing on prior studies, the framework emphasizes data processing, stakeholder support, explainability, and privacy, and is being tested with the Mushroom Hunter/Attention games to demonstrate self-adaptive prompts and personalized stimuli. Preliminary findings report distinct gaze clusters, positional biases, and AoI engagement linked to attentional states, illustrating concrete pathways for teacher interventions. Overall, the approach aims to enhance early detection, targeted intervention, and communication between psychologists, teachers, and students through transparent AI explanations and privacy safeguards.
Abstract
This paper focuses on developing a framework for uncovering insights about NDD children's performance (e.g., raw gaze cluster analysis, duration analysis \& area of interest for sustained attention, stimuli expectancy, loss of focus/motivation, inhibitory control) and informing their teachers. The hypothesis behind this work is that self-adaptation of games can contribute to improving students' well-being and performance by suggesting personalized activities (e.g., highlighting stimuli to increase attention or choosing a difficulty level that matches students' abilities). The aim is to examine how AI can be used to help solve this problem. The results would not only contribute to a better understanding of the problems of NDD children and their teachers but also help psychologists to validate the results against their clinical knowledge, improve communication with patients and identify areas for further investigation, e.g., by explaining the decision made and preserving the children's private data in the learning process.