Active machine learning framework for teaching object recognition skills to children with autism

Active machine learning (AML) techniques enable a machine learning model to perform better with less labeled training data. In this study, we proposed an AML approach for teaching object recognition skills to children with autism spectrum disorders (ASD) and compared its effects with passive learning (PL). A web and touch-based application was developed for teaching object recognition where objects were grouped according to their categories and difficulty levels. The teaching procedure was based on Applied Behavioral Analysis principles. Five children with mild to moderate levels of ASD participated in the study. An alternating treatments design of single-subject research methods was used. The results indicated that AML was more effective than PL for four out of the five participants. Consequently, they can learn faster with fewer teaching trials that are required to reach a learning criterion.