Overview

This project is an AI-powered course creation and delivery platform designed to streamline how educational content is generated, reviewed, and consumed. It supports a structured workflow with three primary roles:

Admin
Tutor (Faculty)
Student

The system combines role-based access control, automated content generation, and a custom-built media pipeline to produce full-length educational videos from simple textual inputs.

Stack: Next.js, Node.js, NestJS, MinIO S3, PostgreSQL, Redis, BullMQ

Authentication & User Flow

To ensure a smooth and minimal-friction experience, authentication is handled exclusively via Google OAuth.

Once a user logs in:

The backend determines whether the user is new or returning
New users are routed to onboarding
Existing users are redirected to their respective dashboards

Onboarding System

Tutor (Faculty) Onboarding

Tutors go through a verification-based onboarding process:

Pay an initial fee of ₹1000
Submit:
- Educational credentials
- Bank account details
- Additional metadata

After submission:

Admin reviews the application
Tutors can be approved or rejected

Only approved tutors can create and publish courses.

Student Onboarding

Students complete a lightweight onboarding process:

Provide basic personal and academic information
No verification is required

Core Features

Student

Browse and enroll in courses
Track learning progress

Tutor

Create courses using natural language input
Define difficulty level and constraints
Review and edit AI-generated course structures
Trigger full course generation

AI-Assisted Course Generation

Tutors provide a simple textual description of a course. The system processes this input to generate:

A structured course roadmap
Chronologically ordered topics and subtopics

Each subtopic is treated as an independent unit and results in a separate video.

Media Generation Pipeline

The platform uses a fully custom-built pipeline to generate videos without relying on traditional video generation systems.

1. Course Breakdown

Each course is structured as:

Main Topic → Subtopics

Each subtopic corresponds to one video.

2. Contextual Input Handling

For each subtopic, the system uses:

Main topic
Current subtopic
Compressed context from previous subtopics (key concepts summary)

This ensures continuity while avoiding redundant data processing.

3. Script & Slide Generation

A structured README file is generated in slide format
Content is organized for presentation-ready output

4. Transcript Generation

A detailed transcript is created
Includes timestamped segments for precise synchronization

5. Audio Generation

Transcript is split based on timestamps
Audio is generated for each segment independently

6. Slide Rendering

Slides are converted into images
Each image corresponds to a specific segment

7. Incremental Video Assembly

Video is built segment by segment
Images and audio clips are aligned using timestamps
Segments are appended progressively

This prevents full regeneration in case of failure.

8. Storage & Access Control

Videos are stored securely
Access is restricted via backend-generated temporary links
Content is served only through the platform domain

Job Processing & Reliability

The system uses a queue-based architecture for handling heavy background tasks.

Each subtopic video is processed as an independent job
Jobs are split into multiple stages:
- Content generation
- Audio generation
- Image generation
- Video compilation

Reliability Features

Maximum of 3 retries per stage
Redis-based progress tracking
Restartable stages with persistent intermediate outputs

State Management

Each video follows a defined state machine:

CREATED → SCRIPT_READY → AUDIO_READY → VIDEO_READY → COMPLETED

This provides:

Clear observability
Easier debugging
Controlled retry mechanisms

System Architecture

Fully containerized using Docker
Backend and services orchestrated locally
Designed for asynchronous processing using queues

Performance Constraints

System currently runs on local infrastructure
Handles approximately 10 concurrent course generations
Beyond this, performance degradation occurs due to resource-heavy processing

What Makes This System Unique

End-to-end automation from text input to video output
Custom-built media generation pipeline
Fine-grained control over each stage of content creation
Strong fault tolerance with resumable workflows

Summary

This project demonstrates how AI can be integrated into a structured system to automate complex educational workflows. Instead of relying on monolithic tools, the platform focuses on modularity, control, and reliability, making it adaptable for future scaling and enhancements.

Blogs

Blogs

Open Education : AI-Powered Course Generation Platform