IEEE Systems, Man and Cybernetics Magazine - October 2021 - 5

A
utism 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.
Autism spectrum disorder ( " autism " ) is a developmental
disorder characterized by significant social, communication,
and behavioral challenges. According to the
National Health Statistics Report [1], the prevalence of
children ages 3-17 years diagnosed with a developmental
disability has increased considerably from 6.99% in 2014-
2016 to 17.8% in 2015-2018. Developmental disabilities can
be severe, long-term disorders often including intellectual
impairments, physical impairments, or both. Intellectual
disabilities, defined by significant limitations in cognition
and adaptive functioning, are some of the most common
impairments diagnosed during the developmental years,
while physical impairments are typically identifiable from
birth. Often, these impairments co-occur and individuals
diagnosed with AUIDD typically require long-term care
and targeted treatment and teaching.
The physical and mental limitations presenting due to
AUIDD affect different aspects of life, including personal
(self-care, independent living) and social skills (keeping
conversations, public speaking), often causing children to
learn and develop slower than a typical child. While there
is no cure for children with AUIDD, there are several types
of treatments such as ABA, occupational therapy, speech
therapy, physical therapy, and pharmacological therapy
available. Each of these treatments have proven effective
in helping individuals with AUIDD achieve a high level of
skill development with earlier treatment leading to larger
gains. Detecting and diagnosing these developmental disorders
early could help apply the needed treatment procedures
and facilitate many students' learning and
functioning to improve the behavior of children over time.
Effective treatment relies on efficient observation. However,
the process of behavioral observation is time-consuming,
requiring clinicians to collect and analyze data [2], use the
collected data to identify the function of a problem behavior
(or what the child is trying to communicate), use collected
data to conduct pattern analysis to categorize problems and
predict categorical outcomes, use assessment data to hypothesize
responsiveness to treatment, design treatment plans
using a hypothesis regarding patient responsiveness to treatment,
and collect ongoing treatment data to detect the effects
of the treatment plan. For example, behavioral assessments
used in ABA, such as the functional behavioral assessment,
are used to detect behavior patterns through indirect observations
(screening instruments), direct observations, and
experimental analysis to identify behavioral function. The
identified function is then matched with a function-based
behavioral intervention [3]. However, since clinicians rely on
human collected data to make decisions, chances of unreliable
decisions are likely high, particularly considering varied
clinical training that may result in differences from one
behavior analyst (BA) clinician to another.
Recent research illustrates the importance of personalized
treatment plans for individuals with AUIDD. However,
while the treatment and education plans may be individualized,
they are often not precise or efficient. In addition, time
spent by clinicians collecting and analyzing the data often
detracts from providing empathetic treatment. Given the high
impact of digital learning platforms, AI, and cloud computing,
the purpose of this study is twofold. The first part describes
how AI in combination with emerging technologies such as
augmented reality (AR) or virtual reality (VR) in the form of a
digital learning platform can benefit more autistic children
and provide a personalized adoptive learning paradigm. The
second part aims to develop conceptual understanding on
how using the efficacy of AI in designing and applying an
appropriate assessment framework for ABA and clinical
interventions can assist clinicians and educators to effectively
assess and monitor each child's behavior and quickly modify
interventions to meet his or her specific needs and to
account for various differences across environments.
Augmenting Teaching and
Precision Treatment
Digital platforms and technologies are argued by many to
have a pivotal role in the dynamics of changing landscape of
clinical treatment. The main arguments include improved
support for treatment by 1) contextualizing and increasing
motivation of students and promoting engagement through
interactive environments and reward structures, 2) providing
a learning experience that caters to pace of patient's
individual learning, and 3) providing continuous and lifelong
learning through mobile learning platforms [4].
October 2021 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE
5

IEEE Systems, Man and Cybernetics Magazine - October 2021

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IEEE Systems, Man and Cybernetics Magazine - October 2021 - Cover1
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IEEE Systems, Man and Cybernetics Magazine - October 2021 - Contents
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IEEE Systems, Man and Cybernetics Magazine - October 2021 - Cover3
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