NeuroAdapt-RAG: A Responsible Large Language Model Framework for Personalized and Accessible Learning Support

Abstract

Abstract—Students with dyslexia, dysgraphia, dyscalculia, attention-deficit/hyperactivity disorder, autism-related support needs, and language-processing difficulties often encounter barriers in standardized digital and classroom instruction. This article presents NeuroAdapt-RAG, a responsible Large Language Model (LLM) framework that combines learner profiling, curriculum-grounded retrieval-augmented generation (RAG), adaptive feedback, multimodal accessibility, progress analytics, and human oversight. The framework converts a learner's academic level, preferred modality, support requirements, mastery state, and interaction history into controlled prompt instructions. Relevant curriculum passages are retrieved from an approved knowledge base and supplied to the LLM to reduce unsupported generation. A safety layer checks age appropriateness, instructional relevance, privacy exposure, and potentially harmful content before delivery. The proposed architecture supports simplified explanations, guided practice, text-to-speech, speech-to-text, teacher/parent dashboards, and auditable learner progress. Functional validation is organized around authentication, profile creation, retrieval, personalization, accessibility, feedback, security, and latency. Because controlled learner-outcome data are not yet available, this study deliberately reports a reproducible evaluation protocol and result template rather than unsupported efficacy claims. The framework provides a practical foundation for inclusive AI tutoring while preserving educator authority and ethical safeguards. Keywords— adaptive learning, inclusive education, large language models, learning disabilities, personalization, retrieval-augmented generation, responsible AI.