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@article{192128,
        author = {Niwas Kumar and Lavit Tyagi and Ratish Raj and Saurabh Kumar and Dr.Neelam Shrivastava},
        title = {A Bilingual AI-Based Screening System for Early Detection of Learning Disorders Using Handwriting and Quiz Features},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1662-1668},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192128},
        abstract = {Early identification of learning disorders such as dyslexia, dysgraphia, and attentional difficulties is crucial for timely intervention. In response to this need, we propose a bilingual (English–Hindi) AI-driven screening tool that integrates handwriting analysis with interactive quiz data. The system employs optical character recognition (OCR) and image preprocessing to extract fine-grained features from students’ written samples (e.g. letter reversals, spacing irregularities) and combines these with response accuracy and latency from short cognitive quizzes. A lightweight interpretable classifier (e.g. decision tree) fuses these multimodal features to output risk levels for learning disorders, accompanied by human-readable explanations. We demonstrate that the prototype achieves promising sensitivity and specificity in flagging at-risk students, substantially augmenting existing resource-intensive screening methods. By embedding the tool in a teacher-facing dashboard, educators can efficiently identify and support struggling learners. Our results suggest that AI-facilitated screening could meaningfully narrow the gap in early detection, especially in under-resourced, multi-lingual settings. Ethical considerations around privacy and consent are discussed.},
        keywords = {Learning disorders, dyslexia, handwriting analysis, early screening, machine learning, bilingual education.},
        month = {February},
        }