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International Journal of Advances in Engineering & Scientific Research

DETECTION OF DYSCALCULIA DISORDER BASED ON COGNITIVE ASSESSMENT USING LLM

Ms.V. Dhanalakshmi, Dhanush R, Kabil V D, Thilip K

Ms.V. Dhanalakshmi, Dhanush R, Kabil V D, Thilip K, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Tamilnadu, India

DOI : https://doi.org/ 10.5281/zenodo.14921547 Page No : 01-11

Published Online : 2025-02-25

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Dyscalculia is a specific learning disorder that affects an individual’s ability to comprehend and manipulate numerical concepts. Early detection of dyscalculia is crucial for effective intervention and academic support. This study proposes a novel approach for dyscalculia detection using a cognitive assessment framework powered by Large Language Models (LLMs). The system evaluates cognitive abilities related to numerical reasoning, working memory, and pattern recognition through interactive question-answer sessions. Leveraging LLMs, the model dynamically adapts difficulty levels based on user responses, ensuring a personalized assessment experience. The responses are analyzed using natural language processing (NLP) techniques and statistical models to identify key indicators of dyscalculia. Furthermore, machine learning algorithms classify individuals based on their cognitive performance, distinguishing between typical mathematical difficulties and potential dyscalculia. The proposed method offers a scalable and efficient alternative to traditional diagnostic methods, reducing the dependency on expert evaluations. Experimental results demonstrate the system's accuracy in identifying dyscalculia traits compared to conventional assessments. This research highlights the potential of AI-driven cognitive evaluations in learning disability detection and paves the way for more accessible and inclusive educational support systems.

Keywords: Dyscalculia, Cognitive Assessment, Large Language Models, ML, NLP, Learning Disability Detection

References

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Dhanalakshmi V, Dhanush R, Kabil V D, & Thilip K (2025), “Detection Of Dyscalculia Disorder Based On Cognitive Assessment Using LLM”, International Journal of Advances in Engineering & Scientific Research, Volume 12, Issue 1, 2025, pp 01-11. DOI : https://doi.org/ 10.5281/zenodo.14921547

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References

Suresh, K., Reddy, P. P., & Preethi, P. (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520.
Yogapriya, J., Saravanabhavan, C., Asokan, R., Vennila, I., Preethi, P., & Nithya, B. (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484.
Barma MD, Muthupandiyan I, Samuel SR, Amaechi BT. Inhibition of Streptococcus mutans, antioxidant property and cytotoxicity of novel nano-zinc oxide varnish. Arch Oral Biol. 2021 Jun;126:105132. doi: 10.1016/j.archoralbio.2021.105132. Epub 2021 Apr 23.
Siddique R, Nivedhitha MS, Jacob B. Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. doi: 10.4103/JCD.JCD_95_19.
Avacharmal, R., Pamulaparthyvenkata, S., Ranjan, P., Mulukuntla, S., Balakrishnan, A., Preethi, P., & Gomathi, R. D. (2024, June). Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
Hu, A. (2024). Developing an ai-based psychometric system for assessing learning difficulties and adaptive system to overcome: A qualitative and conceptual framework. arXiv preprint arXiv:2403.06284.
D’Urso, S., & Sciarrone, F. (2024, June). AI4LA: An intelligent chatbot for supporting students with dyslexia, based on generative AI. In International Conference on Intelligent Tutoring Systems (pp. 369-377). Cham: Springer Nature Switzerland.
Carik, B., Ping, K., Ding, X., & Rho, E. H. (2025). Exploring Large Language Models Through a Neurodivergent Lens: Use, Challenges, Community-Driven Workarounds, and Concerns. Proceedings of the ACM on Human-Computer Interaction, 9(1), 1-28.
Li, R., Kamaraj, A., Ma, J., & Ebling, S. (2024, November). Decoding Ableism in Large Language Models: An Intersectional Approach. In Proceedings of the Third Workshop on NLP for Positive Impact (pp. 232-249).
Kasturi, V. J., & Gupta, S. K. (2020). Computer-assisted Programs for Children with Specific Learning Disorder: Implications and Challenges. In Telerehabilitation in Communication Disorders and Mental Health (pp. 220-241). SAGE Publications Pvt Ltd.

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