Abstract:
his thesis introduces a Student and Faculty Management System with AI support and
accessibility as the priority that automates and simplifies the academic processes of dis abled students. The platform combines all-time voice input, Blind Mode navigation,
AI-controlled assistant support, and student/teacher messaging.
It addresses the limitations of traditional LMS systems, including reliance on visual
interfaces, manual data input, and delayed feedback, which hinder self-directed learning
for visually impaired students.
The system leverages offline speech recognition (Vosk) and Web Speech APIs to allow
users to navigate, dictate, and confirm actions hands-free; utilizes audio emotion-aware
prompts and blind-mode safety controls to provide confidence-level feedback; employs
an AI chatbot (Gemini API) to generate context-sensitive help, interview-like practice
questions, and onboarding instructions; and offers predictive analytics dashboards for
faculty to make data-driven decisions.
The AI voice stack is highly reliable: uninterrupted listening achieves over 98% command
recognition accuracy in Chrome with automatic recovery, while audio announcements and
notifications ensure real-time information accessibility.
By integrating machine learning, natural language interfaces, and multimodal feedback,
this LMS advances AI accessibility by automating the end-to-end academic process,
empowering students and faculty with disabilities to work independently and inclusively
