Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review
Abstract
:1. Introduction
1.1. Mental Disorders
1.1.1. ADHD
1.1.2. Dyslexia
1.1.3. Autism Spectrum Disorders
1.2. Personalised Assistive Tools Using Artificial Intelligence for Children with NDD
2. Current Management Approaches for Children with NDD
2.1. Individualised Educational Approaches for a Child with ADHD
2.2. Individualised Educational Approaches for Children with Dyslexia
2.3. Teaching Support in Schools for Students with ASDs
2.4. Challenges in Implementing Individualised Learning Approaches in Schools
2.5. Use of Artificial Intelligence in Therapies and Supportive Education of Children with Mental Disorders
2.5.1. Conventional Methods Using AI
2.5.2. Advanced Methods Using AI
2.5.3. Importance of AI in Therapies and Supportive Education
3. Materials and Methods
- They described the use of AI tools to help students with ADHD, Dyslexia and/or ASD in their learning:
- They were published between the years 2011 and 2021.
- They were published in a peer-reviewed journal.
- They were published in English.
- They described the use of AI tools to help students with other disorders apart from the NDDs ADHD, dyslexia and/or ASD.
- The article was not published in English.
- The article was not published in a peer-reviewed journal.
- The article was published before 2011.
4. Results
4.1. Summary of Articles Collated
4.2. Effectiveness of AI Tools for Personalised Education
5. Discussion of Main Findings and Results of Study
5.1. Limitations of Existing AI Tools for Personalised Education
5.1.1. Suitable Datasets
5.1.2. Ethical Considerations
5.1.3. Cost of Implementing AI Tools
5.1.4. Information Loss
5.2. Cloud Computing in Schools
5.2.1. Advantages of Using AI in Cloud Computing
5.2.2. Disadvantages of Using AI in Cloud Computing
6. Proposal for a Future AI Tool
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Learning Area | Intervention |
---|---|
Reading comprehension | Establish a sustained silent reading time daily. Allow the child to read a book silently while listening to the teacher reading the story to the whole class. Getting the child to make a storyboard, retell a story during story sessions, role-play characters in a favourite story. Allowing the child to play board games/computer games to enhance reading comprehension skills. Maintaining a word-bank book for words that are hard to read. Providing students with another set of books to be read at home [36] |
Phonics | Teaching children simple reminders on how to learn tougher phonics. Teaching children how to recognise word families for phonetic concepts. Allowing students to play board games, such as Bingo or computer games, to enhance phonics. Using picture–letter charts for children who can identify sounds but not letters [36] |
Writing | Using storyboards to teach students to recognise parts of a story for writing. Creating a post-office in the classroom for students to write and receive letters from their teachers and peers. Using tape recorders to dictate as an alternative to writing or having teacher/peer to write for students who would tell the story [36] |
Spelling | Aligning spelling words to frequently used words by children everyday. Partnering the child with another peer to encourage each other to quiz on spelling words. Using colour-coded letters to help students spell difficult words. Combining movement activities with spelling lessons [36] |
Handwriting | Using special writing paper or teaching how to use a finger spacing to space out each word when writing. Teaching handwriting skills through structured programmes [36] |
Mathematics computation | Using mnemonics to describe fundamental steps easily for Maths computation. Colour-coding arithmetic symbols to provide visual cues, allowing students to use calculators for basic computation, using computer/board games for practicing computations [36] |
Learning Area | Intervention |
---|---|
1. Failure in reading, grouping letters in words. | Using visual perceptions, such as signage or touching letters, to help in reading. Providing simultaneous stimulation of each ear using different sounds [23] |
2. Phonology | Employing strategies that help phonological processing, such as ‘minimal pairs,’ ‘common syllable words,’ and ‘vocal syllabification’ [23] |
3. Grammar | Using grammatical processing strategies, such as ‘syllabification,’ ‘declension of nouns,’ ‘stress,’ and ‘nouns’ [23] |
4. Writing | Using the syntactic approach to teach punctuation and sentences/paragraphs. Using the ‘segmentation with highlighting’ technique for sentence and text segmentation [23] |
Structured Teaching Strategies | Intervention |
---|---|
Physical structure | Establish a supportive classroom environment by creating clear physical or visual boundaries such that expected behaviours for each defined space can be taught and reinforced [39] |
Reducing auditory and visual disturbances | Too much auditory or visual stimuli may hamper processing power; hence, unnecessary distractions are removed in classrooms to help students focus better on concepts taught [39] |
Visual schedules | Implementing visual schedules for the day (instead of using verbal probes), according to the learning needs of each student to enhance student independence and engagement during lessons [39] |
Work system | Implementing a work system for any type of educational activity helps to organise the student by providing a systematic work routine [39] |
Visual structures | Adding a physical or visual aspect to some tasks to help students understand better how an activity needs to be completed [39] |
Boolean Search String | |||
---|---|---|---|
Database | Title | AND [Title/Abstract/Full Text] | No. of Articles |
IEEE | “Autism spectrum disorder”, AND/OR “Attention deficit hyperactivity disorder” AND/OR “dyslexia”, Artificial intelligence AND/OR tools, students AND/OR learning | Machine learning, Neural networks, deep learning | Autism: 0, ADHD: 1, Dyslexia: 0 |
Google Scholar | Autism: 12 100, ADHD: 3900, Dyslexia: 3800 | ||
PubMed | Autism: 0, ADHD: 0, Dyslexia: 0 | ||
Science Direct | Autism: 176, ADHD: 172, Dyslexia: 97 | ||
Springer Link | Autism: 357, ADHD: 179, Dyslexia: 144 |
Author/Year | AI Tool | Features/Model Used for Training | Type of Technology | Learning Area Addressed | Effectiveness |
---|---|---|---|---|---|
AI Assistive Tools Used to Teach Students with ADHD. | |||||
2014 [61] | KAR robot | - | Assistive technology | Improve social skills via storytelling. | Improves children’s cognitive performance. |
2015 [62] | Child activity sensing and training tool |
| Real time assistive technology | Real-time assistive tool that tracks activities and helps students sustain attention. | An assistive intervention that is based on a smartphone and has the potential to aid a child with ADHD who has lost focus in his/her work. |
2018 [63,64] | WatchMinder vibrating watch |
| Wearable technology | Helps to send constant reminders to students to refocus on their work. | The watch has been effective as a simple memory aid for ADHD children with the auditory or vibrating alarm feature. The watch has been found to be affordable, durable, dependable and effective by users [65] |
2018 [63] | Speech recognition software (Dragon Naturallyspeaking(Dragon Sytems company, United States, version 15, /Voice Finger/ViaTalk (LLC Company, New York/Tazti (Voice Tech Group company, United States) |
| Assistive technology | Replaces writing activity with speech to allow students to express themselves efficiently without tiring themselves | Dragon Naturallyspeaking, Voice Finger, Via Talk, Tazti softwares have been reported to be beneficial to students with ADHD and resulted in improvement in the areas of writing, reading and spelling [65] |
2018 [63,67] | Talking calculators |
| Assistive technology | Helps students hear and process numbers easily for mathematics. | Students are able to complete assessments faster with the help of the calculator and has helped students gain independence [67] |
AI assistive tools used to teach students with Dyslexia. | |||||
2013 [68] | Intelligent dyslexic system | Machine learning algorithm, visualisation concept | Assistive technology | Helps students gain knowledge on alphabets and letters | The technology has the potential to improve the reading and writing skills of students. |
2014 [69] | Agent DYSL adaptive reading system | Machine learning algorithm, Mel-frequency cepstral coefficients, discrete cosine transform | Assistive technology | Enables the personalisation of reading environment of Greek students. | Students’ reading pace and accuracy were increased. |
2015 [70] | Computer-based learning model | Machine learning technique | - | Explores the use of machine learning method to improve effectiveness of learning process. | - |
2017 [71] | Applications for reading and writing (Learning Ally, Natural reader, dyslexia quest, sound literacy, ginger page, v books pdf voice reader, openWeb, reading intro by OZ phonics, OCR instantly pro, MindMeister) | Generation of audio files, pytorch deep convolutional text-to-speech models (PytorchDcTts) | Digital application | Helps students with reading and writing skills. | Audiobooks, such as learning Ally, has enabled students to gain confidence, independence and success [72]. |
2018 [73] | DIMMAND, capturaTalk application |
| Digital application | Provides tailored interventions for difficulties encountered in literacy. | Information is not available. |
2020 [74] | Voice dream reader, natural reader, web reader |
| Digital application | Helps with building reading skills. | E-readers have been found to generally improve reading speed and comprehension as compared to reading on paper [75]. |
2020 [76,77] | DytectiveU |
| Digital application | Provides personalised game-based exercises to enhance specific cognitive skills related linked to dyslexia. | The DytectiveU application is reported to be able to offer students a variety of actions that are helpful in the learning of reading and writing [78]. |
2020 [79] | Generative adversarial network | Conversion of image/speech to text | Assistive technology | Converts natural language text to images to aid students in their learning. | - |
AI assistive tools used to teach students with ASD. | |||||
2011 [80] | LIFEisGAME game |
| Digital application | To teach students to recognise facial emotions. | Information is not available. |
2017 [81,82] | ‘Empower me’ application |
| Wearable technology | Encourages social interaction between user and peers/educators. | Students were able to improve their social skills using the Google glass. It was also reported to be fun, useful and engaging [83]. |
2018 [84,85] | Kaspar robot |
| Assistive technology | Helps enhance social interaction skills. | The human-like body and features of Kaspar have been reported to help an ASD student to be more interactive [87]. |
2018 [88] | ABA flashcards- Emotions, Autism emotion, conversation builder, emotions and feelings- autism, Find me, Kid in storybook maker, learning with Rufus, Look in my eyes: Train engineer, Model me going places 2, Pictello, Social stories, Special stories, The social express, Toca Boca |
| Digital application | Teaches social skills | Information is not available. |
2018 [88] | ABA find it, Agnitus, Autism learning games- camp discovery, Intro to letters, Intro to Math, Math Bingo, Pop Math, Starfall ABC, Word wagon |
| Digital application | Helps in different learning areas | The camp discovery enabled participants to show high learning rates over a short period of time. It has been suggested that the application teaches the selected skills effectively [89]. |
2019 [90] | Emotify game |
| Digital application | Helps students to recognise and express feelings. | The application caused participants to experience more engagement and exhibit higher behavioural intentions towards it [91]. |
2019 [92,93] | Milo, NAO, Pepper, Aisoy 1, Keepon robots |
| Assistive technology | Helps build social and communication skills. | Social robots, such as NAO, have been reported to improve social skills in students, especially in terms of eye contact and concentration. Nonverbal children also reportedly started pronouncing some words [95]. |
2020 [88,96] | GoTalks speech generating device, AAC speech buddy, Proloquo2go, talking Larry, Touch chat HD, VAST autism 1-Core |
| Augmentative/alternative communication device | Helps with building communication skills. | Review studies report that high-technology speech generating devices are very effective in teaching manding, intraverbal and multistep tacting to ASD students [98]. |
2020 [96,99,100] | Facesay games |
| Digital application | Software games help recognise behavioural and emotional clues and enhance social skills. | Facesay application is found to be very promising, cost-effective and efficient for teaching affect recognition and mentalising constructs to high-functioning ASD students [101]. |
2020 [102] | Personalised ‘Kiwi’ robot for learning |
| Assistive technology | Adapts lessons according to students’ changing needs. | Kiwi robot has been reported to improve the Maths skills and social skills in ASD students who were part of the study group [103]. |
2020 [96] | Life skills winner application |
| Digital application | Teaches students daily living skills through the application. | Information is not available. |
2020 [103] | PvBOT robot | LEGO Mindstorms EV3 model | Assistive technology | Helps to teach students ‘place value’ concept in Mathematics. | PvBOT is helpful in motivating students to pay attention and stay focused for a longer period. |
2021 [104] | Squizzy educational software | Scrum methodology | Assistive technology | Helps children stay focused during activities that involve cognition, such as colour selection or using pictures. | Effective in the cognitive aspect of therapy. |
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Barua, P.D.; Vicnesh, J.; Gururajan, R.; Oh, S.L.; Palmer, E.; Azizan, M.M.; Kadri, N.A.; Acharya, U.R. Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review. Int. J. Environ. Res. Public Health 2022, 19, 1192. https://doi.org/10.3390/ijerph19031192
Barua PD, Vicnesh J, Gururajan R, Oh SL, Palmer E, Azizan MM, Kadri NA, Acharya UR. Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review. International Journal of Environmental Research and Public Health. 2022; 19(3):1192. https://doi.org/10.3390/ijerph19031192
Chicago/Turabian StyleBarua, Prabal Datta, Jahmunah Vicnesh, Raj Gururajan, Shu Lih Oh, Elizabeth Palmer, Muhammad Mokhzaini Azizan, Nahrizul Adib Kadri, and U. Rajendra Acharya. 2022. "Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review" International Journal of Environmental Research and Public Health 19, no. 3: 1192. https://doi.org/10.3390/ijerph19031192
APA StyleBarua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022). Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review. International Journal of Environmental Research and Public Health, 19(3), 1192. https://doi.org/10.3390/ijerph19031192