Commentary: Adaptive Transformation of Educational Content Using Large Language Models for Children with ADHD

Osama Orabi, Alaa Aldin Hajjar, Hadi Salloum

Track: Tiny paper

Keywords: Education, ADHD, Large Language Models

TL;DR: Personalizing education for children with ADHD using Large Language Models and interactive motor teaching to enhance engagement and learning.

Abstract: Children with Attention Deficit Hyperactivity Disorder (ADHD) face unique challenges in traditional educational settings. While conventional instruction often requires prolonged concentration on material that may not capture their interest, children with ADHD can exhibit deep understanding when engaged with content that resonates with their personal interests. In this paper, we propose a framework that leverages Large Language Models (LLMs) to dynamically transform standard educational material into personalized, engaging content. In addition, we explore the integration of interactive motor teaching techniques to accommodate the natural movement tendencies of these learners. Our approach aims not only to reframe academic material but also to turn the act of learning into a physically engaging process, thereby aligning with the cognitive and behavioral profiles of children with ADHD.

Submission Number: 19