Artificial Intelligence to Strengthen Pedagogical Support for Students with Learning Disorders

Margarita Norela; Doris Iliana Ramírez Apolo; Marcia Fernanda Estacio Dávila; Catalina Beatriz Ureña Garces; Mariuxi Pamela Chica Tomalá

doi:10.63803/prisma.v1n4.33

Authors

Margarita Norela Parroquia San Sebastián del Coca Author https://orcid.org/0009-0003-1988-7609
Doris Iliana Ramírez Apolo Unidad Educativa Ciudad de Ibarra Author
Marcia Fernanda Estacio Dávila Unidad Educativa Ciudad de Ibarra Author
Catalina Beatriz Ureña Garces U.E. 20 de septiembre Author
Mariuxi Pamela Chica Tomalá Unidad Educativa Francisco Huerta Rendón Author https://orcid.org/0000-0002-5857-7035

DOI:

https://doi.org/10.63803/prisma.v1n4.33

Keywords:

Artificial Intelligence, Pedagogical Support, Learning Disorders, Educational Technology, Inclusive Education

Abstract

Learning disorders present persistent challenges to educational systems, often requiring differentiated support mechanisms that exceed the capacity of traditional pedagogical models. Recent advances in artificial intelligence (AI) have introduced transformative possibilities for individualized, adaptive, and evidence-based interventions. This article examines how AI can enhance pedagogical accompaniment for students with learning disorders by integrating diagnostic precision, personalized learning trajectories, and continuous monitoring. Through a systematic review of current literature and analysis of applied case studies, the study highlights the potential of AI-driven tools such as intelligent tutoring systems, natural language processing, and predictive analytics. Findings suggest that AI not only complements the role of educators but also fosters inclusion, engagement, and academic growth in learners with dyslexia, dyscalculia, attention-deficit/hyperactivity disorder (ADHD), and other cognitive challenges. Recommendations are provided to guide future educational policies and practices in leveraging AI for inclusive pedagogy.

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References

Anderson , T., & Shattuck, J. (2012). Design-Based Research: A Decade of Progress in Education Research? Educational Researcher, 41(1), 16-25. https://doi.org/https://doi.org/10.3102/0013189X11428813

Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools. Nesta. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

Braun, V., & Clarke, V. (2021 ). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328-352 . https://doi.org/https://doi.org/10.1080/14780887.2020.1769238

Cerón Silva, S., Ballesteros Lara, M., Salama Muhammad, I., Cerón Silva, D., Cerón Silva, A., & Salazar Rodríguez, R. (2025). Artificial Intelligence as a Co-Teacher: The Future of Personalized Teaching. LATAM Revista Latinoamericana De Ciencias Sociales Y Humanidades, 5(6), 3853–3865. https://doi.org/https://doi.org/10.56712/latam.v5i6.3283

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/https://doi.org/10.4324/9780203771587

Deci, E., & Ryan, R. (2000). The "What" and "Why" of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/https://doi.org/10.1207/S15327965PLI1104_01

Hall, A., & Tannebaum, R. (2013). Test Review: J. L. Wiederholt & B. R. Bryant. (2012). Gray Oral Reading Tests—Fifth Edition (GORT-5). Austin, TX: Pro-Ed. Journal of Psychoeducational Assessment, 31(5), 516-520. https://doi.org/https://doi.org/10.1177/0734282912468578

Holmes, W. (2022). Artificial Intelligence and the Future of Teaching and Learning. OECD Education Working Papers.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education. Promise and Implications for Teaching and Learning. Center for Curriculum Redesign.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/IntelligenceUnleashedSPANISH.pdf

Ma, W., Adesope, O., Nesbit, J., & Liu, Q. (2014). Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/https://doi.org/10.1037/a0037123

McKenney, S., & Reeves, T. (2021). Educational design research: Portraying, conducting, and enhancing productive scholarship. Medical Education, 5(1), 82-92. https://doi.org/https://doi.org/10.1111/medu.14280

Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015). Deep Knowledge Tracing. Computer Science, 1-13. https://doi.org/https://arxiv.org/abs/1506.05908}

Rosli, R. (2011). est Review: A. J. Connolly KeyMath-3 Diagnostic Assessment: Manual Forms A and B. Minneapolis, MN: Pearson, 2007. Journal of Psychoeducational Assessment, 29(1), 94-97. https://doi.org/https://doi.org/10.1177/0734282910370138

Selwyn, N. (2019). Should Robots Replace Teachers?: AI and the Future of Education. Polity Press.

Singer, J., & Willett, J. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. https://doi.org/https://doi.org/10.1093/acprof:oso/9780195152968.001.0001

UNESCO. (2023). UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research

Zaraii Zavaraki, E. (2024). Artificial Intelligence for People with Special Educational Needs. IntechOpen. https://doi.org/10.5772/intechopen.1004158

Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur , F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? Int J Educ Technol High Educ, 16(39 ). https://doi.org/https://doi.org/10.1186/s41239-019-0171-0