ADAPTIVE LANGUAGE AND COMPREHENSION PERSONALIZATION METHODS AND SYSTEMS FOR NEURODIVERSITY READING CONTENT

Abstract

The present invention discloses a computer-implemented method and system for generating adaptive reading material through natural language processing, deep learning, and user profile-based personalization. The system includes an AI-based translation module that first adapts the language of the source text to the user's preferred language, a linguistic analysis module that evaluates readability, syntactic features, and phonological difficulty to calculate a Unified Complexity Score, and an adaptive simplification module that includes deep learning-based linguistic simplification and phonetic simplification enhanced with Kaufman principles and PROMPT to generate more pronounceable forms of language.The system also includes a personalization module to adjust reading style and difficulty based on the user's profile, as well as an adaptive feedback loop that dynamically updates the user's profile based on their reading interactions and phonological performance. Through the integration of these components, the system is able to automatically adjust linguistic complexity, vocabulary selection, syntactic structure, phonological level, and language used, thereby producing digital reading content tailored to each user's abilities, language preferences, and phonological needs. This invention enhances literacy accessibility and supports language development for children, multilingual readers, and neurodiverse individuals with varying speech, language, and phonological abilities.