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Stream/Area:
-
Subject:
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Paper ID:
IJSREM62773
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of computer science and engineering
Sree narayana gurukulam college of engineering
Department of computer science and engineering
Sree narayana gurukulam college of engineering
Department of computer science and engineering
Sree narayana gurukulam college of engineering
Department of computer science and engineering
Sree narayana gurukulam college of engineering
Department of computer science and engineering
Sree narayana gurukulam college of engineering
This project will introduce ZYLO, an AI-powered Dyslexia reading assistant using deep learning and natural Language processing and adaptive interface technologies to Deliver a personalized multi-sensory reading experience for Individuals with dyslexia. Users interact with the system through a web application which processes digital text and transforms it into optimized formats using real-time text Adaptation, syllable segmentation and readability enhancement by means of adjustable font spacing-color. AI Layer Incorporates Text-to-Speech with synchronized providing highlighting and interactive pronunciation feedback to Reinforce reading comprehension. To ensure effective learning This system includes personalized reading speed during progression. control and progress tracking analytics. Having a strong Python Backend architecture, responsive JavaScript frontend and Intelligent adaptation engine, ZYLO redefines traditional reading support tools by making them intelligent, user-centered, and empowering for neurodiverse learners.
KEYWORDS
The integration of adaptive AI technologies with accessible design provides a personalized and effective reading solution for dyslexic readers. By integrating real-time text adaptation, text-to-speech with highlighting, and speech recognition feedback, the proposed system overcomes major limitations in state-of-the-art reading applications. Personalized support, multisensory engagement, and tracking of progress will be ensured without sacrificing usability across a variety of learning contexts. Overall, natural language processing with machine learning and user-centered design come together to provide a comprehensive and smart reading assistant that considerably improves reading proficiency in neurodiverse learners.
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Publication Date
5/8/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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