Children with Attention-Deficit/Hyperactivity Disorder (ADHD) encounter a range of difficulties in their educational journey, particularly related to sustaining attention, maintaining engagement, and managing high levels of distractibility. These challenges often hinder their academic performance and negatively affect their overall learning experience. While current interventions predominantly focus on the screening and diagnosis of ADHD, there is a noticeable gap in the development of effective assistive strategies that actively support children with ADHD in both online and classroom settings. One of the critical challenges faced by educators is maintaining meaningful and consistent engagement with these learners. In this paper, we propose an AI-based framework designed to monitor and enhance attentional engagement in children with ADHD. Leveraging recent advancements in machine learning, the framework utilizes real-time data such as gaze direction, head movement, and body pose to assess the learner’s level of engagement and provide adaptive feedback.