Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication

Wang, Min; Muthu, BalaAnand; Sivaparthipan, C. B.

doi:10.1007/s10772-021-09921-0

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Comparisons with other approaches could not be made, since, to the best of authors’ knowledge after a thorough literature review, this is the first approach that aims at applying AI to estimate the most proper support methodologies in a personalized way for each student. Indeed, AI has been generally used directly to create aid tools, regardless the student-specific needs 19 – 21 , or as a diagnostic tool 10 – 14 . The only study focused on the selection of personalized learning experience based on different dyslexia types is the one proposed in 22 .…”

Section: Discussionmentioning

confidence: 99%

“…This platform is able to adapt the methodology for providing the correct learning approach based on user profile and progress. An interesting approach that explores an alternative way to offer support to dyslexic students was introduced in 21 . Here, AI was employed to develop an augmentative and alternative communication (AAC) model, capable of classifying questions uniquely and to provide users with related pictograms.…”

Section: Introductionmentioning

confidence: 99%

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A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies

Zingoni,

Taborri,

Calabrò

2024

Sci Rep

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Dyslexia is a specific learning disorder that causes issues related to reading, which affects around 10% of the worldwide population. This can compromise comprehension and memorization skills, and result in anxiety and lack of self-esteem, if no support is provided. Moreover, this support should be highly personalized, to be actually helpful. In this paper, a model to classify the most useful methodologies to support students with dyslexia has been created, with a focus on university alumni. The prediction algorithm is based on supervised machine learning techniques; starting from the issues that dyslexic students experience during their career, it is capable of suggesting customized support digital tools and learning strategies for each of them. The algorithm was trained and tested on data acquired through a self-evaluation questionnaire, which was designed and then spread to more than 1200 university students. It allowed 17 useful tools and 22 useful strategies to be detected. The results of the testing showed an average prediction accuracy higher than 90%, which rises to 94% by renouncing to guess the less-predictable 8 tools/strategies. In the light of this, it is possible to state that the implemented algorithm can achieve the set goal and, thus, reduce the gap between dyslexic and non-dyslexic students. This achievement paves the way for a new modality of facing the problem of dyslexia by university institutions, which aims at modifying teaching activities toward students’ needs, instead of simply reducing their study load or duties. This complies with the definition and the aims of inclusivity.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies

Zingoni,

Taborri,

Calabrò

2024

Sci Rep

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“…These problems can significantly impact their academic performance and overall educational outcomes. Learning disorders comprise dyslexia, which means problems in reading; Dysgraphia, which means problems in writing; dyscalculia, which means problems in mathematics; and many more [3]. In the past few years, considerable efforts have been made by India to promote comprehensive education for children suffering from learning disabilities.…”

Section: Introductionmentioning

confidence: 99%

Системний Аналіз Освітніх Інструментів На Основі Штучного Інтелекту Для Людей З Інвалідністю: Досвід Індії

Shivani,

Gupta,

Gupta

2024

ITLT

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Artificial Intelligence (AI) plays a multifaceted role in educating impaired people and has significantly enhanced the learning experience of disabled students. AI is emerging as an influential tool that enhances accessibility, personalizes the learning process, and promotes inclusivity in educating students with disabilities. For visually disabled students, AI-powered braille devices help the children learn and rehearse braille independently. AI can be incorporated into mobile applications and learning platforms invented to help dyslexic students in India. These apps provide many features to make learning fun for learners with disabilities. The Notebook platform utilizes Machine Learning algorithms that provide students with a personalized learning experience. AACDD makes the process of learning easy for children suffering from neuro-developmental disorders. Augmenta11y app utilizes AR (Augmented Reality) to display the content in a form that dyslexic children can easily understand. DYSXA app provides many features for making learning fun for learners with disabilities. Using automation and the latest technologies, Stamurai provides speech therapy to such people at a meager cost. The authors have analyzed and discussed more AI tools developed to help disabled students with education. With the use of these tools, children with disabilities can communicate better, receive an inclusive education, be more accessible, be mentally prepared, and lead independent lives. These tools have shown promising potential in supporting children with disability. It is observed that AI holds much potential to strengthen the learning experiences of disabled students by providing them with personalized support and alleviating the difficulties they face in reading, writing, and learning a language with accuracy.

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Ethical Artificial Intelligence in Telerehabilitation of Neurodevelopmental Disorders: A Position Paper

Castellani

Benassi

Balboni

2023

Lecture Notes in Computer Science

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Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication

Cited by 4 publications

References 27 publications

A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies

A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies

Системний Аналіз Освітніх Інструментів На Основі Штучного Інтелекту Для Людей З Інвалідністю: Досвід Індії

Ethical Artificial Intelligence in Telerehabilitation of Neurodevelopmental Disorders: A Position Paper

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