Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review

Abstract

Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.

Keywords: artificial intelligence; machine learning; mental disorders; neurodevelopmental disorders; personalisation.

Figure 1

(a) Normal brain and (b) ADHD brain with smaller volume.

Figure 2

(a) Large neural adaptation in normal brain and (b) reduced neural adaptation in dyslexic brain.

Figure 3

(a) Neurotypical brain and (b) ASD brain with denser neural connections.

Figure 4

Sequence of steps for training a machine learning model.

Figure 5

Illustration of the CNN model.

Figure 6

Illustration of the LSTM model.

Figure 7

Illustration of the Autoencoder model.

Figure 8

Flowchart detailing the use of PRISMA guidelines for selection of relevant articles.

Figure 9

Pie chart representation of assistive tools used to aid in the learning of ADHD, dyslexia and ASD students.

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Bar graph representation of various assistive tools used to aid in the learning of ADHD, dyslexia and ASD students.

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Benefits of using the cloud system in schools for personalised education.

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Proposed AI-based tool for personalised learning.