An AI Gen and LLM-Based Platform for Pedagogical Support and Sensory Accommodations for Neurodivergent Students
Resumo
Context This research focuses on inclusive, personalized learning for neurodivergent students within educational contexts. The motivation arises from the need for sensory accommodations due to sensory processing challenges commonly experienced by individuals with conditions like ASD and ADHD. Problem Neurodivergent individuals often face sensory integration challenges that impact their school, family, and clinical environments. Without proper accommodations, these challenges hinder development and learning, placing additional demands on teachers and therapists. This problem spans technological, social, and educational dimensions, as inadequate support systems may compromise well-being and intervention effectiveness. Solution The proposed solution is a generative AI platform that uses Small and Large Language Models (SLMs and LLMs) to suggest personalized pedagogical activities and sensory accommodations. It supports teachers, families, and therapists by continuously adjusting approaches to meet students’ individual needs. IS Theory The research is grounded in Sociotechnical Systems Theory, examining the interplay between technology and human factors to ensure a balanced, effective solution within its usage environment. Method A Proof of Concept was employed with qualitative and quantitative methods, including expert interviews and platform testing to gather feedback on functionality and effectiveness. Results Summary Preliminary findings show high user acceptance and improved sensory adaptation for students. The platform demonstrates potential for dynamic, individualized sensory support. Contributions and Impact on IS This research expands generative AI applications in inclusive education, offering an innovative approach for personalized sensory accommodations. Insights from this study could guide academics and practitioners in developing scalable, human-centered support systems for educational and clinical settings.
Palavras-chave:
Neurodiversity, Generative AI, Sensory Accommodations, Inclusive Education, Personalized Learning
Referências
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Kirby, A. V.,Williams, K. L.,Watson, L. R., Sideris, J., Bulluck, J., and Baranek, G. T. Sensory features and family functioning in families of children with autism and developmental disabilities: Longitudinal associations. The American Journal of Occupational Therapy 73, 2 (2019).
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Mattos, J., D’Antino, M., and Cysneiros, R. Tradução para o português do brasil e adaptação cultural do sensory profile. Psicologia - Teoria e Prática 17 (12 2015), 104–120.
Mehta, J., Becker, B. A., Hsin,W.-J., Hummel, J., Kerney, B., and Krupp, B. The influence of generative ai on pedagogy and assessment in computing education. J. Comput. Sci. Coll. 39, 4 (oct 2023), 99.
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Thomas K.F. Chiu, Benjamin Luke Moorhouse, C. S. C., and Ismailov, M. Teacher support and student motivation to learn with artificial intelligence (ai) based chatbot. Interactive Learning Environments 0, 0 (2023), 1–17.
Baidoo-anu, D., and Owusu Ansah, L. Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning. Journal of AI 7, 1 (2023), 52–62.
Borsotti, V., Begel, A., and Bjørn, P. Neurodiversity and the accessible university: Exploring organizational barriers, access labor and opportunities for change. Proc. ACM Hum.-Comput. Interact. 8, CSCW1 (apr 2024).
Chiu, T. K. F. The impact of generative ai (genai) on practices, policies and research direction in education: a case of chatgpt and midjourney. Interactive Learning Environments 0, 0 (2023), 1–17.
Conrad, S. S., and Murphy, D. R. Why neurodivergent tech students are overlooked for jobs and how educators and employers can help. J. Comput. Sci. Coll. 39, 3 (oct 2023), 306–316.
Cooper, G. Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology 32, 3 (June 2023), 444–452.
Currin, F. H., Kilcoin, C., Peterman, K., Rector, K., and Hourcade, J. P. Opportunities and challenges in using tangible, teleoperated voice agents in kid-driven moments in play among families with neurodivergent children. Proc. ACM Hum.-Comput. Interact. 8, CSCW1 (apr 2024).
Fage, C. an emotion regulation app for school inclusion of children with asd: design principles and preliminary results for its evaluation. SIGACCESS Access. Comput. 1, 112 (jul 2015), 8–15.
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., and Li, Q. A survey on rag meeting llms: Towards retrieval-augmented large language models. In 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2024), KDD ’24, Association for Computing Machinery.
Goldberg, H. Unraveling neurodiversity: Insights from neuroscientific perspectives. Encyclopedia 3, 3 (2023), 972–980.
Grandin, T., and Panek, R. The Autistic Brain: Thinking Across the Spectrum. Houghton Mifflin Harcourt, 2013.
Higashida, N., Yoshida, K., and Mitchell, D. The Reason I Jump: The Inner Voice of a Thirteen-Year-Old Boy with Autism. Random House Group, 2013.
INEP. Censo escolar da educação básica, 2021. Brasília: INEP.
Jolly, R. Meeting the needs of children with autism spectrum disorder and their families in hospital settings: The perspectives of certified child life specialists and nurses. The Journal of Child Life: Psychosocial Theory and Practice (2015).
Kirby, A. V.,Williams, K. L.,Watson, L. R., Sideris, J., Bulluck, J., and Baranek, G. T. Sensory features and family functioning in families of children with autism and developmental disabilities: Longitudinal associations. The American Journal of Occupational Therapy 73, 2 (2019).
Leal, M. V. S. The Therapeutic Companion in the school context: Some conceptions. Our Knowledge Publishing, 2024.
Lo, C. K. What is the impact of chatgpt on education? a rapid review of the literature. Education Sciences 13, 4 (2023), 410.
Mattos, J., D’Antino, M., and Cysneiros, R. Tradução para o português do brasil e adaptação cultural do sensory profile. Psicologia - Teoria e Prática 17 (12 2015), 104–120.
Mehta, J., Becker, B. A., Hsin,W.-J., Hummel, J., Kerney, B., and Krupp, B. The influence of generative ai on pedagogy and assessment in computing education. J. Comput. Sci. Coll. 39, 4 (oct 2023), 99.
Prakash, K., Rao, S., Hamza, R., Lukich, J., Chaudhari, V., and Nandi, A. Integrating llms into database systems education. In 3rd International Workshop on Data Systems Education: Bridging education practice with education research (New York, NY, USA, 2024), DataEd ’24, Association for Computing Machinery.
Qadir, J. Engineering education in the era of chatgpt: Promise and pitfalls of generative ai for education. Journal of Engineering Education (2023).
Rakap, S. Chatting with gpt: Enhancing individualized education program goal development for novice special education teachers. Journal of Special Education Technology (2023).
Sağdiç, A., Elumar-Efe, E., and Sani-Bozkurt, S. Genai, robots, and inclusive special education: Autism spectrum disorder in the age of generative ai. Journal of Special Education Technology (2023).
Seiradakis, E. V. Unpacking experts’ opinions on chatgpt potential assistive roles and risks in early childhood special education. In New Media Pedagogy: Practice and Implementation in Early Childhood Education. Springer, 2024, pp. 380–392.
Strzelecki, A. To use or not to use chatgpt in higher education: A study of students’ acceptance and use of technology. Journal of Educational Technology (2023).
Thomas K.F. Chiu, Benjamin Luke Moorhouse, C. S. C., and Ismailov, M. Teacher support and student motivation to learn with artificial intelligence (ai) based chatbot. Interactive Learning Environments 0, 0 (2023), 1–17.
Publicado
19/05/2025
Como Citar
ROCHA, Vanderlene C.; QUEIROZ, Jacqueline T. de; SILVA, Carlo M. R. da.
An AI Gen and LLM-Based Platform for Pedagogical Support and Sensory Accommodations for Neurodivergent Students. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 231-240.
DOI: https://doi.org/10.5753/sbsi.2025.246443.