Objective

To build a platform that automatically generates relevant tags and metadata for lecture videos using AI and NLP, making content easily searchable, organized, and accessible for students and instructors.

Key Features

Student Panel:

• Search lecture videos using tags, keywords, or topics

• Filter content by subject, difficulty, or tag relevance

• View suggested related videos based on tags

Teacher Panel:

• Upload lecture videos

• Automatically generate tags, keywords, and brief video summaries

• Edit or approve AI-generated tags

• View analytics on which tags are most searched

Admin Panel:

• Manage users (students & teachers)

• Monitor tagging accuracy and system performance

• Configure AI model settings (keyword extraction, tagging rules)

Tech Stack

Workflow (Step-by-Step)

1. Video Upload

• Teacher logs in and uploads a lecture video

• Video metadata (title, subject, instructor) is recorded

2. Audio Extraction & Transcription

• AI engine extracts audio from video

• Speech-to-text transcription converts lecture to text

3. Tag Generation

• NLP analyzes transcription for key topics, concepts, and terms

• Tags are ranked based on frequency, context, and importance

• AI suggests additional tags based on course content and similar videos

4. Review & Editing

• Teachers review and approve or edit AI-generated tags

• Tags are saved and associated with video in database

5. Search & Recommendation

• Students can search videos using tags or keywords

• System suggests related videos using tag similarity