Digital Cognitive Behavioral Therapy Tools for Students with Emotional and Behavioral Disorders

Abstract

Online Cognitive Behavioral Therapy (CBT) applications are increasingly being used as a method to assist students with Emotional and Behavioral Disorders (EBD), but the conventional mode of delivery has accessibility, personalization, and engagement difficulties. This paper discusses these gaps by suggesting an AI-inspired digital CBT model specifically to be used to work with schools. The point is to determine the efficiency of adaptive CBT modules that combine the features of emotion recognition, progress monitoring, and the real-time feedback mechanism. The mixed-method was used, which entailed experimental design, and pre-to-post evaluation of 180 students with EBD. It used machine learning models to detect the behavioral patterns and reinforcement learning to apply custom therapy progressions. The emotional regulation, behavioral stability, and academic engagement were measured through quantitative analysis whereas usability and acceptance were captured in the qualitative feedback. Findings indicate that significant improvements were made in a range of important metrics: emotional regulation change was better by 41.8, behavioral incidents changed by 36.5 and student engagement improved by 47.2 points compared to the baseline. Also, there was a reduction of response time in delivering intervention by 52.6, which is an indicator of efficiency in the system. This study is applicable to the inclusive education settings and digital mental health solutions that can be scaled in resource-limited settings. The paper finds that digital CBT tools with the addition of an AI-powered personalization can greatly enhance the results of a therapeutic intervention and accessibility and provide a strong framework in future educational and clinical practice. Moreover, the framework empowers teachers and caregivers by providing dashboard (insights) and early risk warning signals to facilitate proactive interventions and joint decision-making procedures to promote long-term student wellbeing and institutional effectiveness in a variety of learning settings and populations globally and sustainably.