Leveraging Artificial Intelligence to Identify Students with Learning Challenges
Keywords:
artificial intelligence; early identification; educational technology; learning difficulties; studentsAbstract
Globally, over 79.2 million individuals are affected by learning disabilities, with a rising prevalence that challenges educational systems, especially in resource-limited settings. This study explored the effectiveness of artificial intelligence (AI)-based tools in identifying students with learning difficulties (SWLD) in Jordanian schools and investigated educators’ perceptions toward these tools. Guided by the universal design for learning (UDL) and information processing theory (IPT), a mixed-methods research design was adopted. Data were collected between September and November 2024 through an online survey administered to 150 educational professionals, including teachers, school administrators, and policymakers. After excluding pilot respondents, 130 valid responses were analyzed, yielding an 87% response rate. Quantitative data were evaluated using descriptive and inferential statistics (multiple linear regression via SPSS), while qualitative responses underwent thematic analysis. Findings revealed that AI tools—particularly machine learning and natural language processing—were perceived as highly effective in the early identification of learning challenges. Additionally, educators’ positive perceptions significantly predicted AI integration in schools, although concerns about ethical use and data security were noted. The study underscores the necessity of training and equitable access to AI technologies to support inclusive education. These results offer practical and policy-level implications for integrating AI into special education frameworks in Jordan.
https://doi.org/10.26803/ijlter.24.5.32
References
Abdul Hamid, S. S., Admodisastro, N., Mansour, N., Ghani, A. A. A., & Kamaruddin, A. (2018a). Engagement prediction in the adaptive learning model for students with dyslexia [Conference session]. Proceedings of the 4th International Conference on Human–Computer Interaction and User Experience in Indonesia, CHIuXiD '18 (pp. 66?73). https://doi.org/10.1145/3287820.3287836
Abdul Hamid, S. S., Admodisastro, N., Mansour, N., Kamaruddin, A., & Ghani, A. A. A. (2018b). Dyslexia adaptive learning model: Student engagement prediction using machine learning approach. In R. Ghazali (Ed.), Recent advances on soft computing and data mining (pp. 372–384). Springer International Publishing.
Al-Mahrezi, A., Al-Futaisi, A., & Al-Mamari, W. (2016). Learning disabilities: Opportunities and challenges in Oman. Sultan Qaboos Univiversity Medical Journal, 16(2), e129?e131. https://doi.org/10.18295/squmj.2016.16.02.001
Alsobhi, A. Y., & Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalized learning experiences based on learning style and dyslexia type. Data Technologies and Applications, 53(2), 189–200. https://doi.org/10.1108/DTA-10-2018-0092
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. https://doi.org/10.1037/cap0000111
Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence, & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory (Vol. 2, pp. 89–195). Academic Press.
Barua, 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), Article 1192. https://doi.org/10.3390/ijerph19031192
Binns, R. (2018). Ethical considerations of AI in education: Issues, challenges, and opportunities. AI and Education Journal, 5(2), 17–29. https://doi.org/10.1145/3200872.3200874
Brynjolfsson, E., & McAfee, A. (2017). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
CAST. (2018). Universal design for learning guidelines version 2.2. http://udlguidelines.cast.org
Dominguez O, & Carugno P. (2023). Learning disability. StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK554371/
Drigas, A., & Ioannidou, R.-E. (2013). A review on arti?cial intelligence in special education. In M. D. Lytras, D. Ruan, R. D. Tennyson, P. Ordonez de Pablos, F. J. García Peñalvo, & L. Rusu (Eds.), Information systems, e-learning, and knowledge management research (pp. 385–391). Springer.
Fletcher, J. M., Lyon, G. R., Fuchs, L. S., & Barnes, M. A. (2019). Learning disabilities: From identification to intervention. The Guilford Press.
Flogie, A., Aberšek, B., Kordigel Aberšek, M., Sik Lanyi, C., & Pesek, I. (2020). Development and evaluation of intelligent serious games for children with learning dif?culties: Observational study. JMIR Serious Games, 8(2), e13190. https://doi.org/10.2196/13190
Gligorea, I., Cioca, M., Oancea, R., Gorski, A-T., Gorski, H., & Tudorache, P. (2024). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12), Article 1216. https://doi.org/10.3390/educsci13121216
Gupta, R. (2019). Adaptive testing tool for students with dyslexia [Conference session]. Proceedings of the 2019 China-Qatar International Workshop on Artificial Intelligence and Applications to Intelligent Manufacturing (AIAIM) (pp. 11–16), 1–4 January 2019, Doha, Qatar. IEEE. 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. https://jise.org/Volume33/n1/JISE2022v33n1pp98-108.html
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Kumar, K., & Wideman, H. (2021). Using artificial intelligence to support students with dyslexia and dysgraphia: An application of UDL principles. Journal of Educational Technology Development and Exchange (JETDE), 14(1), 1–17. https://doi.org/10.18785/jetde.1401.01
Käser, T., Busetto, A. G., Solenthaler, B., Baschera, G.-M., Kohn, J., Kucian, K., von Aster, M., & Gross, M. (2013). Modeling and optimizing mathematics learning in children. International Journal of Arti?cial Intelligence in Education, 23(1), 115–135. https://doi.org/10.1007/s40593-013-0003-7
Kerr, D. (2021). Teacher training and the effective use of AI in the classroom. International Journal of Educational Technology, 20(3), 56–67. https://doi.org/10.1007/s10210-021-01475-2
Kohli, A., Sharma, S., & Padhy, S. K. (2018). Specific learning disabilities: Issues that remain unanswered. Indian Journal of Psychological Medicine, 40(5), 399–405. https://doi.org/10.4103/IJPSYM.IJPSYM_86_18
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. https://doi.org/10.3233/978-1-61499-566-1-180
Learning Disabilities Association of America (LDA). (2022). Understanding learning disabilities: An overview for educators and parents. https://ldaamerica.org
McCloskey, M., & Rapp, B. (2017). Developmental dysgraphia: An overview and framework for research. Cognitive Neuropsychology, 34(3–4), 65–82. https://doi.org/10.1080/02643294.2017.1369016
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
Moreau, D., & Waldie, K. E. (2015). Developmental learning disorders: From generic interventions to individualized remediation. Frontiers in Psychology, 6, Article 2053. https://doi.org/10.3389/fpsyg.2015.02053
Mubin, O., Shahid, S., & Al Mahmud, A. (2017). Exploring machine learning applications in educational systems. International Journal of Artificial Intelligence in Education, 27(3), 501–524. https://doi.org/10.1007/s40593-017-0145-7
Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. CAST Professional Publishing. Retrieved from https://udltheorypractice.cast.org
National Center for Education Statistics (2024, May). Students with disabilities. U.S. Department of Education, Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator/cgg/students-with-disabilitiessuggested-citation
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. https://doi.org/10.1108/JAT-11-2012-0036
Ouherrou, N., Elhammoumi, O., Benmarrakchi, F., & El Ka?, J. (2019). Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in the virtual learning environment. Education and Information Technologies, 24(2), 1777–1792. https://doi.org/10.1007/s10639-018-09852-5
Panjwani-Charani, S., & Zhai, X. (2023). AI for students with learning disabilities: A systematic review. In X. Zhai, & J. Krajcik (Eds.), Uses of arti?cial intelligence in STEM education (Chapter 21). Oxford University Press.
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, Article 9955212. https://doi.org/10.1155/2021/9955212
Ponto J. (2015). Understanding and evaluating survey research. Journal of the Advanced Practitioner in Oncology, 6(2), 168–171. https://www.researchgate.net/publication/286445115
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. https://www.researchgate.net/publication/358635044
Rai, H. L., Saluja, N., & Pimplapure, A. (2023). AI and learning disabilities: Ethical and social considerations in educational technology. Educational Administration: Theory and Practice, 29(4), 01–08. https://doi.org/10.53555/kuey.v29i4.5693
Rajapakse, S., Polwattage, D., Guruge, U., Jayathilaka, I., Edirisinghe, T., & Thelijjagoda, S. (2018). ALEXZA: A mobile application for people with dyslexia utilizing arti?cial intelligence and machine learning concepts [Conference session]. 2018 3rd International Conference on Information Technology Research (ICITR), 5–7 December 2018, Moratuwa, Sri Lanka. IEEE. https://doi.org/10.1109/icitr.2018.8736130
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for Supervision and Curriculum Development (ASCD).
Samoili, S., López Cobo, M., Delipetrev, B., Martínez-Plumed, F., Gómez, E., & de Prato, G. (2021). AI watch. De?ning Arti?cial Intelligence 2.0: Towards an operational de?nition and taxonomy for the AI landscape. Publications Office of the European Union. https://www.researchgate.net/publication/363538481
Schmid, A., Rolf, M., & Fischer, A. (2020). Teacher training and AI adoption in classrooms: Barriers and solutions. Journal of Technology in Education and Learning, 17(1), 11–29. https://doi.org/10.1007/s10639-019-1034-9
Seitz, M. (2020). Ethical AI in education: Data privacy and bias in algorithms. Journal of Educational Ethics, 29(4), 45–60. https://doi.org/10.1016/j.edutec.2020.10.004
Sharif, M. S., & Elmedany, W. (2022). A proposed machine learning-based approach to support students with learning dif?culties in the post-pandemic norm [Conference session]. 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1988–1993), 28–31 March 2022, Tunis, Tunisia. IEEE. https://doi.org/10.1109/EDUCON52537.2022.9766690
Swanson, H. L., & Jerman, O. (2007). The influence of working memory on reading growth in subgroups of children with reading disabilities. Journal of Experimental Child Psychology, 96(4), 249–283. https://doi.org/10.1016/j.jecp.2006.12.004
Taherdoost, H. (2021). Data collection methods and tools for research: A step-by-step guide to choose data collection technique for academic and business research projects. International Journal of Academic Research in Management (IJARM), 10(1), 10–38. https://www.researchgate.net/publication/359596426
Tang, T. L., & Wong, H. (2018). Natural language processing tools for detecting dyslexia: A review. Journal of Special Education Technology, 33(2), 87–102.
UNICEF. (2021). Nearly 240 million children with disabilities around the world, UNICEF’s most comprehensive statistical analysis ?nds. UNICEF. https://www.unicef.org/rosa/press-releases/nearly-240-million-children-disabilities-around-world-unicefs-most-comprehensive
U.S. Department of Education. (2004). Individuals with Disabilities Education Act (IDEA). https://sites.ed.gov/idea/
Wang, M., Muthu, B., & Sivaparthipan, C. B. (2021). Smart assistance to dyslexia students using arti?cial intelligence-based augmentative alternative communication. International Journal of Speech Technology, 25, 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 [Conference session]. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper No. 516, pp. 1–14). https://doi.org/10.1145/3290605.3300746
Yates, J., & Gikandi, J. (2018). Reducing diagnostic time with machine learning in educational assessments. Computers & Education, 123, 112–125.
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. https://doi.org/10.1080/03057267.2020.1735757
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/DOI:10.1002/tea.21885
Zhao, Z., Chuah, J. H., Lai, K. W., Chow, C. O., Gochoo, M., Dhanalakshmi, S., Wang, N., Bao, W., & Wu, X. (2023). Conventional machine learning and deep learning in Alzheimer’s disease diagnosis using neuroimaging: A review. Frontiers in Computational Neuroscience, 17, Article 1038636. https://doi.org/10.3389/fncom.2023.1038636
Zhou, C., Liu, Y., & Xu, Y. (2022). Detecting cognitive overload in students using machine learning and physiological data: Implications for adaptive learning systems. Educational Technology Research and Development, 70(1), 143–162. https://doi.org/10.1007/s11423-021-10050-2
Zhou, Z., Wu, Q., & Yang, L. (2022). AI-driven cognitive intervention systems: A new approach to support dyslexic learners. Computers & Education, 187, Article 104548. https://doi.org/10.1016/j.compedu.2022.104548
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, Article 4624. https://doi.org/10.3390/app11104624
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