Aecting one in every 68 children, Autism Spectrum Disorder (ASD) is one of the fastest growing developmental disabilities. Scientic research has proven that early behavioral intervention can improve learning, communication, and social skills. Similarly, studies have shown that the usage of off-the-shelf technology boosts motivation in children diagnosed with ASD while increasing their attention span and ability to interact socially. Embracing perspectives from different fields of study can lead to the development of an effective tool to complement traditional treatment of those with ASD. This thesis documents the re-engineering, extension, and evolution of Ying, an existing web application designed to aid in the learning of autistic children. The original methodology of Ying combines expertise from other research areas including developmental psychology, semantic learning, and computer science. In this work, Ying is modied to incorporate aspects of traditional treatment, such as Applied Behavior Analysis. Using cutting-edge software technology in areas like voice recognition and mobile device applications, this project aspires to use software engineering approaches and audio-visual interaction with the learner to enhance social behavior and reinforce verbal communication skills in children with ASD, while detecting and storing learning patterns for later study.