Abstract
Dyslexia students frequently deal with multiple difficulties in daily life, involving social interactions throughout their lives. Sometimes they are quickly refused the chance to indulge in social events since they suffer difficulty in learning, reading, understanding, etc. AAC seems to be a vital communication aid for dyslexia students by providing an augmented reality (AR) paradigm to effective learning. This paper enhances the existing learning assistance technologies with innovative Artificial Intelligence (AI) to reinvigorate the Augmentative Alternative Communication (A2C) model for dyslexia children. The AI-based Augmentative Alternative Communication Approach has been developed to enhance learning skills with dyslexia by adapting to practices, and learning models are cognitively considered. The work on the academic skills of dyslexia students has been improved through the AI-based alternative communication paradigm for the improvement of the students with reading and learning. The AI-based AAC (AI–A2C) integrates the hybrid AI classifier in AAC to classify unique questions and provide users with the most appropriate pictograms. In contrast to the standard application, the proposed classifier decreased the effort and time taken to interact by 36.56% and 66.34%. Furthermore, the proposed model's performance is evaluated by its accuracy and efficiency of the hybrid AI classifier and compared with other AI classifiers.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Armstrong, A., & Gutica, M. (2020). Bootstrapping: The emergent technological practices of postsecondary students with mathematics learning disabilities. Exceptionality Education International, 30(1), 1.
Ascari, R. E., Pereira, R., & Silva, L. (2018). Mobile interaction for augmentative and alternative communication: A systematic mapping. SBC Journal on Interactive Systems, 9(2), 105–118.
Baglama, B., Yucesoy, Y., & Yikmis, A. (2018). Using animation as a means of enhancing learning of individuals with special needs. T.E.M. Journal, 7(3), 670.
Barua, D. (2020). Assisted technology for cognitive comprehension in the differently abled. International Journal of English Learning & Teaching Skills, 2(4), 1642–1659.
Basheer, S., Gandhi, U. D., Priyan, M. K., & Parthasarathy, P. (2019). Network support data analysis for fault identification using machine learning. International Journal of Software Innovation (IJSI)., 7(2), 41–49.
Bhattacharya, S., Kaluri, R., Singh, S., Alazab, M., & Tariq, U. (2020). A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics, 9(2), 219.
Chelkowski, L., Yan, Z., & Asaro-Saddler, K. (2019). The use of mobile devices with students with disabilities: A literature review. Preventing School Failure: Alternative Education for Children and Youth, 63(3), 277–295.
Chirvasiu, N. D., & Simion-Blândă, E. (2018). Alternative and augmentative communication in support of persons with language development retardation. Romanian Journal for Multidimensional Education/Revista Romaneasca Pentru Educatie Multidimensionala. https://doi.org/10.18662/rrem/43
Giannouli, V., & Banou, M. (2020). The intelligibility and comprehension of synthetic versus natural speech in dyslexic students. Disability and Rehabilitation: Assistive Technology, 15(8), 898–907.
Ikeshita, H. (2020). Japanese public library services for dyslexic children. Journal of Librarianship and Information Science., 52(2), 485–492.
Ji, F., Hsu, C. H., & Montenegro-Marin, C. E. (2020). Evaluating and recognizing stressful periods and events of urban migrant children from microblog. Current Psychology, 24, 1–9.
Jones, A. D., Jagannathan, K. A., Rhoades, A., Srivastava, A. K., Grotjahn, R., & Ullrich, P. A. (2018). Decision-relevant metrics for regional hydroclimate phenomena. AGUFM, 2018, GC14C-01.
Kadry, S., Roufayel, R. (201). How to use effectively smartphone in the classroom. In 2017 IEEE global engineering education conference (EDUCON) (pp. 441–447). IEEE.
Karapetsas, A. V., Laskaraki, R. M., Karapetsa, A. A., Mitropoulou, A. G., Bampou, M. D. (2019, July 19). The essential role of innovative technologies in assessment and rehabilitation settings. In: International conference on digital transformation and global society (pp. 672–679). Cham: Springer.
Khamparia, A., Singh, S. K., Luhach, A. K., & Gao, X. Z. (2020). Classification and analysis of users review using different classification techniques in intelligent e-learning system. International Journal of Intelligent Information and Database Systems, 13(2–4), 139–149.
Lv, Z., Yang, H. A., Singh, A. K., Manogaran, G., & Lv, H. (2020). Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Transactions on Industrial Informatics., 17, 1496–1504.
Manickam, A., Ezhilmaran, D., & Soundrapandiyan, R. (2017). Local adjacent extrema pattern for fingerprint image classification. IOP Conference Series: Materials Science and Engineering, 263(4), 042143.
Manogaran, G., Rawal, B. S., Saravanan, V., Kumar, P. M., Martínez, O. S., Crespo, R. G., Montenegro-Marin, C. E., & Krishnamoorthy, S. (2020). Blockchain based integrated security measure for reliable service delegation in 6G communication environment. Computer Communications, 1(161), 248–256.
McNicholl, A., Casey, H., Desmond, D., & Gallagher, P. (2019). The impact of assistive technology use for students with disabilities in higher education: A systematic review. Disability and Rehabilitation: Assistive Technology, 18, 1–4.
Opie, J. (2018). Educating students with vision impairment today: Consideration of the expanded core curriculum. British Journal of Visual Impairment, 36(1), 75–89.
Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2018). Patient-centric I.C.T.s based healthcare for students with learning, physical and/or sensory disabilities. Telematics and Informatics, 35(4), 654–664.
Prathik, A., Anuradha, J., & Uma, K. (2018). Survey on spatial data mining, challenges and its applications. Journal of Computational and Theoretical Nanoscience, 15(9–10), 407.
Rose, R., & Shevlin, M. (2020). Support provision for students with Special Educational Needs in Irish Primary Schools. Journal of Research in Special Educational Needs, 20(1), 51–63.
Samuel, O., Javaid, S., Javaid, N., Ahmed, S. H., Afzal, M. K., & Ishmanov, F. (2018). An efficient power scheduling in smart homes using Jaya based optimization with time-of-use and critical peak pricing schemes. Energies, 11(11), 3155.
Sanchez-Gordon, S. (2020). Striving for inclusion in E-learning and E-health. Latin American Women and Research Contributions to the IT Field, 18, 44–72.
Schneps, M. H., Chen, C., Pomplun, M., Wang, J., Crosby, A. D., & Kent, K. (2019). Pushing the speed of assistive technologies for reading. Mind, Brain, and Education, 13(1), 14–29.
Shankar, A., & Jaisankar, N. (2018). Dynamicity of the scout bee phase for an Artificial Bee Colony for optimized cluster head and network parameters for energy efficient sensor routing. SIMULATION, 94(9), 835–847.
Shankar, K., Perumal, E., Elhoseny, M., & Nguyen, P. T. (2021). An IoT-cloud based intelligent computer-aided diagnosis of diabetic retinopathy stage classification using deep learning approach. CMC-Computers Materials & Continua, 66(2), 1665–1680.
Sravankumar, B., Anilkumar, C., Easwaramoorthy, S., Ramasubbareddy, S., & Govinda, K. (2019). Iterative sharpening of digital images. In J. K. Mandal, S. C. Satapathy, M. K. Sanyal, P. P. Sarkar, & A. Mukhopadhyay (Eds.), Information systems design and intelligent applications (pp. 53–62). Springer.
Stauter, D. W., Prehn, J., Peters, M., Jeffries, L. M., Sylvester, L., Wang, H., & Dionne, C. (2019). Assistive technology for literacy in students with physical disabilities: A systematic review. Journal of Special Education Technology, 34(4), 284–292.
Tanwar, S., Obaidat, M. S., Tyagi, S., & Kumar, N. (2019). Online signature-based biometric recognition. In M. S. Obaidat, I. Traore, & I. Woungang (Eds.), Biometric-based physical and cybersecurity systems (pp. 255–285). Springer.
Tiron, K., & Gherguţ, A. (2019). The predictors of dyslexia in a regular orthography. Annals of AII. Cuza University. Psychology Series, 28, 67–90.
Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of artificial intelligence and machine learning in smart cities. Computer Communications, 154, 313–323.
Usman, O. L., & Muniyandi, R. C. (2020). CryptoDL: Predicting dyslexia biomarkers from encrypted neuroimaging dataset using energy-efficient residue number system and deep convolutional neural network. Symmetry, 12(5), 836.
Xiang, X., Li, Q., Khan, S., & Khalaf, O. I. (2021). Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review, 86, 106515.
Xiao, H., Muthu, B., & Kadry, S. N. (2020). Artificial intelligence with robotics for advanced manufacturing industry using robot-assisted mixed-integer programming model. Intelligent Service Robotics., 17, 1.
Zhang, H., Jolfaei, A., & Alazab, M. (2019). A face emotion recognition method using convolutional neural network and image edge computing. IEEE Access, 28(7), 159081–159089.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, M., Muthu, B. & Sivaparthipan, C.B. Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication. Int J Speech Technol 25, 343–353 (2022). https://doi.org/10.1007/s10772-021-09921-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-021-09921-0