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

Author

Listed:
  • Prabal Datta Barua

    (School of Business, University of Southern Queensland, Springfield 4300, Australia
    Faculty of Engineering and Information Technology, University of Technology, Sydney 2007, Australia)

  • Jahmunah Vicnesh

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore)

  • Raj Gururajan

    (School of Business, University of Southern Queensland, Springfield 4300, Australia)

  • Shu Lih Oh

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore)

  • Elizabeth Palmer

    (School of Woman’s and Children’s Health, University of New South Wales, Sydney 2031, Australia
    Centre for Clinical Genetics, Sydney Children’s Hospital, Randwick, New South Wales 2031, Australia)

  • Muhammad Mokhzaini Azizan

    (Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, Nilai 71800, Malaysia)

  • Nahrizul Adib Kadri

    (Department of Biomedical Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia)

  • U. Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
    School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung City 41354, Taiwan)

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.

Suggested Citation

  • Prabal Datta Barua & Jahmunah Vicnesh & Raj Gururajan & Shu Lih Oh & Elizabeth Palmer & Muhammad Mokhzaini Azizan & Nahrizul Adib Kadri & U. Rajendra Acharya, 2022. "Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review," IJERPH, MDPI, vol. 19(3), pages 1-26, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1192-:d:730454
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    References listed on IDEAS

    as
    1. The-Hanh Pham & Jahmunah Vicnesh & Joel Koh En Wei & Shu Lih Oh & N. Arunkumar & Enas. W. Abdulhay & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals," IJERPH, MDPI, vol. 17(3), pages 1-15, February.
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