Artificial Intelligence Applications for Students with Learning Disabilities: A Systematic Review
Keywords:
learning challenges, synthetic intelligence, developmental reading disability, number dyslexia, tailored education, technology integration model for learners with disabilities
Abstract
This review study endeavors to elucidate the utilization of artificial intelligence (AI) in assisting students with learning disabilities (SWLDs). Among the 16 studies examined, dyslexia was the primary focus in 10 instances, with only one study concentrating on dyscalculia, and the remainder addressing learning disabilities in a broader context. Notably, only half of the studies targeted school-age children. Across these studies, seven distinct categories of AI applications were identified, encompassing adaptive learning, facial expression analysis, chatbots, communication aides, mastery learning systems, intelligent tutoring systems, and interactive robots. Among these, adaptive learning emerged as the most prevalent. Employing the SAMR-LD (Substitute, Augment, Modify, Redefine - Learning Disability) model, it was discerned that AI has been applied in diverse capacities to support SWLDs, with instances observed across substitution, augmentation, modification, and redefinition levels. While the findings underscore the potential of AI in aiding SWLDs, the limited number of empirical studies also highlights substantial gaps, indicating the necessity for further research into AI's broader role beyond mere identification and diagnosis of learning disabilities in this population.
References
Abdul Hamid, S. S., Admodisastro, N., Manshor, N., Ghani, A. A. A., & Kamaruddin, A. (2018a). Engagement prediction in the adaptive learning model for students with dyslexia. Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD ’18, 66–73.
Abdul Hamid, S. S., Admodisastro, N., Manshor, N., Kamaruddin, A., & Ghani, A. A. A. (2018b). Dyslexia adaptive learning model: Student engagement prediction using machine learning approach. Recent Advances on Soft Computing and Data Mining, 372–384.
Asghar, A., Sladeczek, I. E., Mercier, J., & Beaudoin, E. (2017). Learning in science, technology, engineering, and mathematics: Supporting students with learning disabilities. Canadian Psychology / Psychologie Canadienne, 58(3), 238–249.
Büttner, G., & Hasselhorn, M. (2011). Learning disabilities: Debates on definitions, causes, subtypes, and responses. International Journal of Disability, Development and Education, 58(1), 75–87.
Drigas, A., & Ioannidou, R.-E. (2013). A review on artificial intelligence in special education. Communications in Computer and Information Science, 278, 385–391.
Flogie, A., Aberšek, B., Kordigel Aberšek, M., Sik Lanyi, C., & Pesek, I. (2020). Development and evaluation of intelligent serious games for children with learning difficulties: Observational study. JMIR Serious Games, 8(2), e13190.
Gupta, R. (2019). Adaptive testing tool for students with dyslexia. In 2019 China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM). https://doi.org/10.1109/aiaim.2019.8632775
Gupta, S., & Chen, Y. (2022). Supporting Inclusive Learning Using Chatbots? A Chatbot-Led Interview Study. Journal of Information Systems Education, 33(1), 98-108.
Käser, T., Busetto, A. G., Solenthaler, B., Baschera, G.-M., Kohn, J., Kucian, K., von Aster, M., & Gross, M. (2013). Modelling and optimizing mathematics learning in children. International Journal of Artificial Intelligence in Education, 23(1), 115–135.
Latif, S., Tariq, R., Tariq, S., & Latif, R. (2015). Designing an assistive learning aid for writing acquisition: A challenge for children with dyslexia. Studies in Health Technology and Informatics, 217, 180–188.
Learning Disabilities Association of America. (n.d.). Types of learning disabilities. Learning Disabilities Association of America. Retrieved May 25, 2022, from https://ldaamerica.org/types-of-learning-disabilities/
Moats, L. C. (2006). How spelling supports reading and why it is more regular and predictable than you may think. American Educator, 12–43.
National Center for Education Statistics. (2022). Students with disabilities.
Ndombo, D. M., Ojo, S., & Osunmakinde, I. O. (2013). An intelligent integrative assistive system for dyslexic learners. Journal of Assistive Technologies, 7(3), 172–187
Ouherrou, N., Elhammoumi, O., Benmarrakchi, F., & El Kafi, J. (2019). Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in virtual learning environment. Education and Information Technologies, 24(2), 1777–1792.
Papakostas, G. A., Sidiropoulos, G. K., Lytridis, C., Bazinas, C., Kaburlasos, V. G., Kourampa, E., Karageorgiou, E., Kechayas, P., & Papadopoulou, M. T. (2021). Estimating children engagement interacting with robots in special education using machine learning. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/9955212
Poornappriya, T. S., & Gopinath, R. (2020). Application of machine learning techniques for improving learning disabilities. International Journal of Electrical Engineering and Technology, 11(10), 403–411.
Puentedura, R. R. (2006). Transformation, technology, and education. http://hippasus.com/resources/tte/puenteduratte.pdf
Rajapakse, S., Polwattage, D., Guruge, U., Jayathilaka, I., Edirisinghe, T., & Thelijjagoda, S. (2018). ALEXZA: A mobile application for dyslexics utilizing artificial intelligence and machine learning Concepts. In 2018 3rd International Conference on Information Technology Research (ICITR). https://doi.org/10.1109/icitr.2018.8736130
Rauschenberger, M., Lins, C., Rousselle, N., Hein, A., & Fudickar, S. (2019). Designing a new puzzle app to target dyslexia screening in pre-readers. Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good, 155–159.
Rello, L., Romero, E., Rauschenberger, M., Ali, A., Williams, K., Bigham, J. P., & White, N. C. (2018). Screening dyslexia for English using HCI measures and machine learning. Proceedings of the 2018 International Conference on Digital Health, 80–84.
Samoili, S., López Cobo, M., Gómez, E., De Prato, G., Martínez-Plumed, F., & Delipetrev, B. (2020). AI watch defining artificial intelligence towards an operational definition and taxonomy of artificial intelligence. Joint Research Centre (JRC).
Seymour, P. H. K., Aro, M., & Erskine, J. M. (2003). Foundation literacy acquisition in European orthographies. British Journal of Psychology, 94(Pt 2), 143–174.
Sharif, M. S., & Elmedany, W. (2022). A proposed machine learning based approach to support students with learning difficulties in the post-pandemic norm. 2022 IEEE Global Engineering Education Conference (EDUCON), 1988–1993.
Terada, Y. (2020, May 4). A powerful model for understanding good tech integration. Edutopia. https://www.edutopia.org/article/powerful-model-understanding-good-tech-integration/
Thompson, W., Li, H., & Bolen, A. (n.d.). Artificial intelligence, machine learning, deep learning and beyond. SAS Institute. Retrieved August 22, 2022, from https://www.sas.com/enus/insights/articles/big
UNICEF. (2021, November). Nearly 240 million children with disabilities around the world, UNICEF’s most comprehensive statistical analysis finds. UNICEF. https://www.unicef.org/rosa/press-releases/nearly-240-million-children-disabilitiesaround-world-unicefs-most-comprehensive
Wang, M., Muthu, B., & Sivaparthipan, C. B. (2021). Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication. International Journal of Speech Technology, 25(2), 343–353. https://doi.org/10.1007/s10772-021-09921-0
Wu, S., Reynolds, L., Li, X., & Guzmán, F. (2019). Design and evaluation of a social media writing support tool for people with dyslexia. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–14.
Yaquob, A. A., & Hamed, A. K. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2), 189–200.
Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology, 30(2), 1-11.
Zhai, X., & Nehm, R. (2023). AI and formative assessment: The train has left the station. Journal of Research in Science Teaching, 60(6), 1390-1398. https://doi.org/10.1002/tea.21885
Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020). Applying machine learning in science assessment: a systematic review. Studies in Science Education, 56(1), 111-151.
Zhai, X., Zhang, M., Li, M., & Zhang, X. (2019). Understanding the relationship between levels of mobile technology use in high school physics classrooms and the learning outcome. British Journal of Educational Technology: Journal of the Council for Educational Technology, 50(2), 750–766.
Zingoni, A., Taborri, J., Panetti, V., Bonechi, S., Aparicio-Martínez, P., Pinzi, S., & Calabrò, G. (2021). Investigating issues and needs of dyslexic students at university: proof of concept of an artificial intelligence and virtual reality-based supporting platform and preliminary results. Applied Sciences, 11(10). https://doi.org/10.3390/app11104624
Zvoncak, V., Mekyska, J., Safarova, K., Smekal, Z., & Brezany, P. (2019). New approach of dysgraphic handwriting analysis based on the tunable q-factor wavelet transform. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). https://doi.org/10.23919/mipro.2019.8756872
Abdul Hamid, S. S., Admodisastro, N., Manshor, N., Kamaruddin, A., & Ghani, A. A. A. (2018b). Dyslexia adaptive learning model: Student engagement prediction using machine learning approach. Recent Advances on Soft Computing and Data Mining, 372–384.
Asghar, A., Sladeczek, I. E., Mercier, J., & Beaudoin, E. (2017). Learning in science, technology, engineering, and mathematics: Supporting students with learning disabilities. Canadian Psychology / Psychologie Canadienne, 58(3), 238–249.
Büttner, G., & Hasselhorn, M. (2011). Learning disabilities: Debates on definitions, causes, subtypes, and responses. International Journal of Disability, Development and Education, 58(1), 75–87.
Drigas, A., & Ioannidou, R.-E. (2013). A review on artificial intelligence in special education. Communications in Computer and Information Science, 278, 385–391.
Flogie, A., Aberšek, B., Kordigel Aberšek, M., Sik Lanyi, C., & Pesek, I. (2020). Development and evaluation of intelligent serious games for children with learning difficulties: Observational study. JMIR Serious Games, 8(2), e13190.
Gupta, R. (2019). Adaptive testing tool for students with dyslexia. In 2019 China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM). https://doi.org/10.1109/aiaim.2019.8632775
Gupta, S., & Chen, Y. (2022). Supporting Inclusive Learning Using Chatbots? A Chatbot-Led Interview Study. Journal of Information Systems Education, 33(1), 98-108.
Käser, T., Busetto, A. G., Solenthaler, B., Baschera, G.-M., Kohn, J., Kucian, K., von Aster, M., & Gross, M. (2013). Modelling and optimizing mathematics learning in children. International Journal of Artificial Intelligence in Education, 23(1), 115–135.
Latif, S., Tariq, R., Tariq, S., & Latif, R. (2015). Designing an assistive learning aid for writing acquisition: A challenge for children with dyslexia. Studies in Health Technology and Informatics, 217, 180–188.
Learning Disabilities Association of America. (n.d.). Types of learning disabilities. Learning Disabilities Association of America. Retrieved May 25, 2022, from https://ldaamerica.org/types-of-learning-disabilities/
Moats, L. C. (2006). How spelling supports reading and why it is more regular and predictable than you may think. American Educator, 12–43.
National Center for Education Statistics. (2022). Students with disabilities.
Ndombo, D. M., Ojo, S., & Osunmakinde, I. O. (2013). An intelligent integrative assistive system for dyslexic learners. Journal of Assistive Technologies, 7(3), 172–187
Ouherrou, N., Elhammoumi, O., Benmarrakchi, F., & El Kafi, J. (2019). Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in virtual learning environment. Education and Information Technologies, 24(2), 1777–1792.
Papakostas, G. A., Sidiropoulos, G. K., Lytridis, C., Bazinas, C., Kaburlasos, V. G., Kourampa, E., Karageorgiou, E., Kechayas, P., & Papadopoulou, M. T. (2021). Estimating children engagement interacting with robots in special education using machine learning. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/9955212
Poornappriya, T. S., & Gopinath, R. (2020). Application of machine learning techniques for improving learning disabilities. International Journal of Electrical Engineering and Technology, 11(10), 403–411.
Puentedura, R. R. (2006). Transformation, technology, and education. http://hippasus.com/resources/tte/puenteduratte.pdf
Rajapakse, S., Polwattage, D., Guruge, U., Jayathilaka, I., Edirisinghe, T., & Thelijjagoda, S. (2018). ALEXZA: A mobile application for dyslexics utilizing artificial intelligence and machine learning Concepts. In 2018 3rd International Conference on Information Technology Research (ICITR). https://doi.org/10.1109/icitr.2018.8736130
Rauschenberger, M., Lins, C., Rousselle, N., Hein, A., & Fudickar, S. (2019). Designing a new puzzle app to target dyslexia screening in pre-readers. Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good, 155–159.
Rello, L., Romero, E., Rauschenberger, M., Ali, A., Williams, K., Bigham, J. P., & White, N. C. (2018). Screening dyslexia for English using HCI measures and machine learning. Proceedings of the 2018 International Conference on Digital Health, 80–84.
Samoili, S., López Cobo, M., Gómez, E., De Prato, G., Martínez-Plumed, F., & Delipetrev, B. (2020). AI watch defining artificial intelligence towards an operational definition and taxonomy of artificial intelligence. Joint Research Centre (JRC).
Seymour, P. H. K., Aro, M., & Erskine, J. M. (2003). Foundation literacy acquisition in European orthographies. British Journal of Psychology, 94(Pt 2), 143–174.
Sharif, M. S., & Elmedany, W. (2022). A proposed machine learning based approach to support students with learning difficulties in the post-pandemic norm. 2022 IEEE Global Engineering Education Conference (EDUCON), 1988–1993.
Terada, Y. (2020, May 4). A powerful model for understanding good tech integration. Edutopia. https://www.edutopia.org/article/powerful-model-understanding-good-tech-integration/
Thompson, W., Li, H., & Bolen, A. (n.d.). Artificial intelligence, machine learning, deep learning and beyond. SAS Institute. Retrieved August 22, 2022, from https://www.sas.com/enus/insights/articles/big
UNICEF. (2021, November). Nearly 240 million children with disabilities around the world, UNICEF’s most comprehensive statistical analysis finds. UNICEF. https://www.unicef.org/rosa/press-releases/nearly-240-million-children-disabilitiesaround-world-unicefs-most-comprehensive
Wang, M., Muthu, B., & Sivaparthipan, C. B. (2021). Smart assistance to dyslexia students using artificial intelligence based augmentative alternative communication. International Journal of Speech Technology, 25(2), 343–353. https://doi.org/10.1007/s10772-021-09921-0
Wu, S., Reynolds, L., Li, X., & Guzmán, F. (2019). Design and evaluation of a social media writing support tool for people with dyslexia. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–14.
Yaquob, A. A., & Hamed, A. K. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2), 189–200.
Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology, 30(2), 1-11.
Zhai, X., & Nehm, R. (2023). AI and formative assessment: The train has left the station. Journal of Research in Science Teaching, 60(6), 1390-1398. https://doi.org/10.1002/tea.21885
Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020). Applying machine learning in science assessment: a systematic review. Studies in Science Education, 56(1), 111-151.
Zhai, X., Zhang, M., Li, M., & Zhang, X. (2019). Understanding the relationship between levels of mobile technology use in high school physics classrooms and the learning outcome. British Journal of Educational Technology: Journal of the Council for Educational Technology, 50(2), 750–766.
Zingoni, A., Taborri, J., Panetti, V., Bonechi, S., Aparicio-Martínez, P., Pinzi, S., & Calabrò, G. (2021). Investigating issues and needs of dyslexic students at university: proof of concept of an artificial intelligence and virtual reality-based supporting platform and preliminary results. Applied Sciences, 11(10). https://doi.org/10.3390/app11104624
Zvoncak, V., Mekyska, J., Safarova, K., Smekal, Z., & Brezany, P. (2019). New approach of dysgraphic handwriting analysis based on the tunable q-factor wavelet transform. In 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). https://doi.org/10.23919/mipro.2019.8756872
Published
2024-03-25
How to Cite
Bhatti, I., Mohi-U-din, S. F., Hayat, Y., & Tariq, M. (2024). Artificial Intelligence Applications for Students with Learning Disabilities: A Systematic Review. European Journal of Science, Innovation and Technology, 4(2), 40-56. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/397
Issue
Section
Articles
Copyright (c) 2024 Iftikhar Bhatti, Syed Farooq Mohi-U-din, Yawar Hayat, Mehtab Tariq
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN 
