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.