Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review
Article
Article Title | Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review |
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ERA Journal ID | 44293 |
Article Category | Article |
Authors | Barua, Prabal Datta, Jahmunah, Jahmunah, Gururajan, Raj, Oh, Shu Lih, Palmer, Elizabeth, Azizan, Muhammad Mokhzaini, Kadri, Nahrizul Adib and Acharya, U. Rajendra |
Journal Title | International Journal of Environmental Research and Public Health |
Journal Citation | 19 (3) |
Article Number | 1192 |
Number of Pages | 26 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1660-4601 |
1661-7827 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph19031192 |
Web Address (URL) | https://www.mdpi.com/1660-4601/19/3/1192 |
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 | neurodevelopmental disorders; machine learning; artificial intelligence; personalisation; mental disorders |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | School of Business |
University of Technology Sydney | |
Ngee Ann Polytechnic, Singapore | |
University of New South Wales | |
Sydney Children's Hospital, Australia | |
Islamic Science University of Malaysia, Malaysia | |
University of Malaya, Malaysia | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z01q6/artificial-intelligence-enabled-personalised-assistive-tools-to-enhance-education-of-children-with-neurodevelopmental-disorders-a-review
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