Artificial Intelligence in Special Education: A Literature Review

Authors

  • Toluwani Victor Aliu University of Chester Author

DOI:

https://doi.org/10.31181/sa22202424

Keywords:

Artificial intelligence, Assistive technology, Special educational needs, LLMs

Abstract

New avenues for communication with students who have Special Educational Needs (SEN) have begun to open up because of innovative educational technology. The most successful strategies over the past 20 years (2001–2020) have been those that utilize Artificial Intelligence (AI) techniques. The quality of life for SEN students is thought to be improved by the efficient use of AI techniques. Therefore, it becomes necessary to implement A.I. approaches to design procedures for both diagnosis and intervention. In addition, the study focuses on  Assistive Technology (AT) and provides information on the most pertinent research conducted over the previous 20 years for the earlier objectives. This paper also discusses the limitations of current AI use in special education, including the scarcity of intervention tools and the lack of standardized diagnostic methods. Furthermore, the review explores the unique challenges in developing countries in implementing AI-based solutions for special education. This review study advances the available research by reviewing the role of Large Language Models (LLMs) as an AT while building on existing research that has acknowledged the much earlier potential of AI technologies to empower special education.

References

‎[1] ‎ Adeniran, A. O., Oyeniran, G. T., Adeniran, A. A., & Mosunmola, M. J. (2024). Digitization in logistics ‎and its effect on sustainability in Nigeria. ‎https://www.researchgate.net/profile/AdedayoAdeniran/publication/379839886.‎

‎[2] ‎ Ertel, W. (2018). Introduction to artificial intelligence. Springer. ‎https://books.google.com/books?id=geFHDwAAQBAJ&dq

‎[3] ‎ Saugirouglu, c., Erler, M., & Becsdok, E. (2003). Artificial intelligence applications in engineering - i : ‎artificial neural networks. Ufuk Kitabevi. https://avesis.gazi.edu.tr/yayin/007771d1-7aa2-4756‎

‎[4] ‎ Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson. ‎https://thuvienso.hoasen.edu.vn/handle/123456789/8967.‎

‎[5] ‎ Sen, Z. (2018). Significance of artificial intelligence in science and technology. Journal of intelligent ‎systems: theory and applications, 1(1), 1–4. https://dergipark.org.tr/en/pub/jista/issue/35722/398325‎

‎[6] ‎ Sen, N., & Akbay, T. (2023). Artificial intelligence and innovative applications in special education. ‎Instructional technology and lifelong learning, 4(2), 176–199. https://doi.org/10.52911/itall.1297978‎

‎[7] ‎ Lanzilotti, R., & Roselli, T. (2007). An experimental evaluation of Logiocando, an intelligent tutoring ‎hypermedia system. International journal of artificial intelligence in education, 17(1), 41–56.‎

‎[8] ‎ Wu, T. K., Meng, Y. R., & Huang, S. C. (2006). Application of artificial neural network to the identification of ‎students with learning disabilities. IC-AI (pp. 162–168). https://www.cloudzilla.ai/dev-‎education/introduction-to-random-forest-in-machine-learning/#get-started

‎[9] ‎ Gupta, I., & Nagpal, G. (2020). Artificial intelligence and expert systems. Mercury Learning and ‎Information. https://books.google.com/books?‎

‎[11] ‎ Mbaabu, O. (2020). Introduction to Random Forest in Machine Learning. https://www.cloudzilla.ai/dev-‎education/introduction-to-random-forest-in-machine-learning/#get-started

‎[12] ‎ Soto, M. G., & Adeli, H. (2017). Multi-agent replicator controller for sustainable vibration control of ‎smart structures. Journal of vibroengineering, 19(6), 4300–4322. https://doi.org/10.21595/jve.2017.18924‎

‎[13] ‎ Berndsen, M., & McGarty, C. (2012). Perspective taking and opinions about forms of reparation for ‎victims of historical harm. Personality and social psychology bulletin, 38(10), 1316–1328. ‎https://doi.org/10.1177/0146167212450322‎

‎[14] ‎ Kubat, M. (2017). An introduction to machine learning. Springer. https://doi.org/10.1007/978-3-319-63913-0‎

‎[15] ‎ Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual ‎understanding: a review. Neurocomputing, 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116‎

‎[16] ‎ Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-‎‎9958(65)90241-X

‎[17] ‎ Bleak, K. W., & Abernathy, T. (2022). Individuals with disabilities education act. Individuals with ‎disabilities education act (IDEA). google scholar worldcat fulltext. https://doi.org/10.4324/9780367198459-‎REPRW196-1‎

‎[18] ‎ Nanni, L., & Lumini, A. (2009). Ensemble generation and feature selection for the identification of ‎students with learning disabilities. Expert systems with applications, 36(2), 3896–3900. ‎https://doi.org/10.1016/j.eswa.2008.02.065‎

‎[19] ‎ Georgopoulos, V. C., Malandraki, G. A., & Stylios, C. D. (2003). A fuzzy cognitive map approach to ‎differential diagnosis of specific language impairment. Artificial intelligence in medicine, 29(3), 261–278. ‎https://doi.org/10.1016/S0933-3657(02)00076-3‎

‎[20] ‎ Rebolledo-Mendez, G., & De Freitas, S. (2008). Attention modeling using inputs from a brain computer ‎interface and user-generated data in second life. The tenth international conference on multimodal ‎interfaces. Chania. https://d1wqtxts1xzle7.cloudfront.net/102652240/Attention_20modeling-‎libre.pdf?1685049449‎

‎[21] ‎ Rebolledo-Mendez, G., Dunwell, I., Martinez-Mirón, E. A., Vargas-Cerdán, M. D., De Freitas, S., ‎Liarokapis, F., & Garcia-Gaona, A. R. (2009). Assessing neurosky’s usability to detect attention levels in ‎an assessment exercise. 13th international conference, HCI international 2009 (pp. 149–158). Berlin. ‎Springer. https://doi.org/10.1007/978-3-642-02574-7_17

‎[22] ‎ Arthi, K., & Tamilarasi, A. (2008). Prediction of autistic disorder using neuro fuzzy system by applying ‎ANN technique. International journal of developmental neuroscience, 26(7), 699–704. ‎https://doi.org/10.1016/j.ijdevneu.2008.07.013‎

‎[23] ‎ Hernandez, J., Mousalli, G., & Rivas, F. (2009). Expert system for the diagnosis of learning difficulties in ‎children’s basic education. WSEAS international conference. proceedings. mathematics and computers in ‎science and engineering. https://www.researchgate.net

‎[24] ‎ Hernadez, J., Mousalli, G., & Rivas, F. (2009). Learning difficulties diagnosis for children’s basic ‎education using expert systems. WSEAS transactions on information science and applications, 7(6), 1–25. ‎https://www.researchgate.net

‎[25] ‎ Jain, K., Manghirmalani, P., Dongardive, J., & Abraham, S. (2009). Computational diagnosis of learning ‎disability. International journal of recent trends in engineering, 2(3), 64. ‎https://d1wqtxts1xzle7.cloudfront.net

‎[26] ‎ ‎] Kohli, M., & Prasad, T. V. (2010). Identifying dyslexic students by using artificial neural networks. ‎Proceedings of the world congress on engineering (Vol. 1, pp. 1–4). ‎https://www.researchgate.net/profile/TPrasad/publication

‎[27] ‎ Anuradha, J., Tisha, Ramachandran, V., Arulalan, K. V, & Tripathy, B. K. (2010). Diagnosis of adhd using ‎svm algorithm. Proceedings of the third annual acm bangalore conference (pp. 1–4). Association for ‎Computing Machinery. https://doi.org/10.1145/1754288.1754317‎

‎[28] ‎ Melis, E., Andres, E., Budenbender, J., Frischauf, A., Goduadze, G., Libbrecht, P., …& Ullrich, C. (2001). ‎ActiveMath: a generic and adaptive web-based learning environment. International journal of artificial ‎intelligence in education, 12, 385–407. https://telearn.hal.science/hal-00197329/‎

‎[29] ‎ Melis, E., & Siekmann, J. (2004). Activemath: an intelligent tutoring system for mathematics [presentation]. ‎International conference on artificial intelligence and soft computing (pp. 91–101). ‎https://doi.org/10.1007/978-3-540-24844-6_12‎

‎[30] ‎ Schipor, O. A., Pentiuc, S. G., & Schipor, M. D. (2010). Improving computer based speech therapy using ‎a fuzzy expert system. Computing and informatics, 29(2), 303–318. ‎https://www.cai.sk/ojs/index.php/cai/article/view/85‎

‎[31] ‎ Riedl, M., Arriaga, R., Boujarwah, F., Hong, H., Isbell, J., & Heflin, J. (2009). Graphical social scenarios: ‎toward intervention and authoring for adolescents with high functioning autism. 2009 AAAI fall symposium ‎series. https://cdn.aaai.org/ocs/945/945-4144-1-PB.pdf

‎[32] ‎ Drigas, A., Kouremenos, D., & Vrettaros, J. (2008). Teaching of english to hearing impaired individuals ‎whose mother language is the sign language. Emerging technologies and information systems for the ‎knowledge society: first world summit on the knowledge society, wsks (pp. 263–270). Athens, Greece: Springer. ‎https://doi.org/10.1007/978-3-540-87781-3_29‎

‎[33] ‎ Gonzalez, C. S., Guerra, D., Sanabria, H., Moreno, L., Noda, M. A., & Bruno, A. (2010). Automatic ‎system for the detection and analysis of errors to support the personalized feedback. Expert systems ‎with applications, 37(1), 140–148. https://doi.org/10.1016/j.eswa.2009.05.027‎

‎[34] ‎ Baschera, G.-M., & Gross, M. (2010). Poisson-based inference for perturbation models in adaptive ‎spelling training. International journal of artificial intelligence in education, 20(4), 333–360. ‎https://content.iospress.com/articles/international-journal-of-artificial-intelligence-in-education/jai011‎

‎[35] ‎ Malik, I. (2020). Practice of universal design in administration staff in higher education. [Thesis]. ‎https://oda.oslomet.no/oda-xmlui/handle/10642/9251‎

‎[36] ‎ Manjari, K., Verma, M., & Singal, G. (2020). A survey on assistive technology for visually impaired. ‎Internet of things, 11, 100188. https://doi.org/10.1016/j.iot.2020.100188‎

‎[37] ‎ Newman, S. A., & Gopalkrishnan, S. (2023). The prospect of digital human communication for ‎organizational purposes. Frontiers in communication, 8, 1200985. ‎https://doi.org/10.3389/fcomm.2023.1200985‎

‎[38] ‎ Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., …& others. (2020). Language ‎models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901. ‎https://doi.org/10.48550/arXiv.2005.14165‎

‎[39] ‎ Vaswani, A. (2017). Attention is all you need. ArXiv preprint arxiv:1706.03762. ‎https://user.phil.hhu.de/~cwurm/wp-content/uploads/2020/01/7181-attention-is-all-you-need.pdf

‎[40] ‎ Zheng, L., Long, M., Zhong, L., & Gyasi, J. F. (2022). The effectiveness of technology-facilitated ‎personalized learning on learning achievements and learning perceptions: a meta-analysis. Education ‎and information technologies, 27(8), 11807–11830. https://doi.org/10.1007/s10639-022-11092-7‎

‎[41] ‎ Bryant, B. R., & Seay, P. C. (1998). The technology-related assistance to individuals with disabilities act: ‎Relevance to individuals with learning disabilities and their advocates. Journal of learning disabilities, ‎‎31(1), 4–15. https://doi.org/10.1177/002221949803100102‎

‎[42] ‎ Burgstahler, S. (2003). The role of technology in preparing youth with disabilities for postsecondary ‎education and employment. Journal of special education technology, 18(4), 7–19. ‎https://doi.org/10.1177/016264340301800401‎

Published

2024-09-01

How to Cite

Aliu, T. V. . (2024). Artificial Intelligence in Special Education: A Literature Review. Systemic Analytics, 2(2), 188-199. https://doi.org/10.31181/sa22202424