Next-Generation Support for Learning Disabilities: Artificial Intelligence, Machine Learning, and Emerging Interventions

Learning Disabilities (LDs) encompass a range of neurodevelopmental conditions including dyslexia, dysgraphia, dyscalculia that impede learning despite adequate intelligence and opportunity. Their heterogeneity demands early, precise, and contextually relevant identification. Advances in Machine Learning (ML) have enabled the early detection of LDs through pattern recognition in academic, cognitive, and behavioural datasets, outperforming many traditional screening tools. Algorithms such as Support Vector Machines, Random Forests, Naïve Bayes can identify at-risk learners with high accuracy, facilitating timely, personalized interventions. Technological innovations, including Artificial Intelligence (AI) based learning platforms, adaptive tutoring systems, and predictive analytics, are redefining support for students with LDs. Assistive Technology (AT), from text-to-speech/speech-to-text systems to cognitive training software enhances accessibility and engagement, while ML-driven personalization aligns tools to individual learning profiles. This study synthesizes current advancements in machine learning and AI for early detection and personalized intervention in learning disabilities, proposing a multidisciplinary, ethically grounded roadmap for inclusive and culturally sensitive educational transformation.