AI -Based Tutor for Visually Impaired Students

Visually impaired students typically face intense
difficulties when handling electronic learning materials such as PDF
textbooks, academic papers, and study guides. Traditional assistive
technologies like screen readers have the basic text-to-speech
functionality without enabling smart interaction and understanding
document structure. This paper outlines the design and
implementation of an AI-Based Tutor system to enhance the
learning experience for visually impaired students. The system
integrates Optical Character Recognition (OCR) to extract
structured text from diverse PDF structures, including accurate line
and section detection. The system has high-quality Text-to-Speech
(TTS) capabilities for natural-sounding speech, and a Speech-toText (STT) functionality for real-time voice queries and
instructions. Essentially, the system employs leading-edge Natural
Language Processing (NLP) models such as GPT or Gemini to
provide intelligent question answering, context-based
conversations, and content summarization. A voice-enabled
interface affords hands-free and intuitive control of the learning
process. Preliminary evaluation with blind students shows increased
accessibility, comprehension, and interaction efficacy compared to
existing solutions. The current paper contributes a new, integrated
solution that provides blind students greater independence and
control over their studies