  Autism spectrum disorder is a developmental disorder characterized by
significant social, communication, and behavioral challenges. Individuals
diagnosed with autism, intellectual, and developmental disabilities (AUIDD)
typically require long-term care and targeted treatment and teaching. Effective
treatment of AUIDD relies on efficient and careful behavioral observations done
by trained applied behavioral analysts (ABAs). However, this process
overburdens ABAs by requiring the clinicians to collect and analyze data,
identify the problem behaviors, conduct pattern analysis to categorize and
predict categorical outcomes, hypothesize responsiveness to treatments, and
detect the effects of treatment plans. Successful integration of digital
technologies into clinical decision-making pipelines and the advancements in
automated decision-making using Artificial Intelligence (AI) algorithms
highlights the importance of augmenting teaching and treatments using novel
algorithms and high-fidelity sensors. In this article, we present an
AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to
provide personalized treatment and learning plans to AUIDD individuals. By
defining systematic experiments along with automated data collection and
analysis, AI-ABA can promote self-regulative behavior using reinforcement-based
augmented or virtual reality and other mobile platforms. Thus, AI-ABA could
assist clinicians to focus on making precise data-driven decisions and increase
the quality of individualized interventions for individuals with AUIDD.
