TAI - Teaching Assistant Intelligence

Autonomous AI-powered grading system to reduce TA workload by automatically grading student submissions from handwritten PDFs.

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Overview

TAI (Teaching Assistant Intelligence) is a Next.js application that automates the grading process for educational assignments. It uses multi-engine OCR to extract handwritten work from PDFs, Claude AI to grade submissions based on rubrics, and provides a comprehensive interface for both instructors and students.

Key Features

• Multi-Engine OCR: Extract handwritten answers from PDFs using Claude, PaddleOCR, Pix2Text, and Surya
• AI-Powered Grading: Automated grading using Claude Sonnet 4.5 with rubric-based assessment
• Dual-Mode Interface:
  • TA/Instructor view: Create assignments, manage rubrics, review submissions
  • Student view: Submit work, view grades and feedback
• Consensus Algorithm: Multiple OCR engines work together for higher accuracy
• Solution Key Extraction: Automatically parse solution PDFs to create answer keys
• Real-time Feedback: Detailed per-question feedback for students

Architecture

Tech Stack

• Frontend: Next.js 16, React 19, TypeScript, Tailwind CSS
• UI Components: Radix UI, Shadcn/ui
• AI/ML: Anthropic Claude API, multiple OCR engines
• Infrastructure: Docker (OCR services), Redis (caching)
• Charts: Recharts for analytics visualization

System Components

┌─────────────────────────────────────────────────────────────┐
│                     Next.js Application                     │
│  ┌─────────────────┐  ┌──────────────┐  ┌───────────────┐  │
│  │   TA Interface  │  │   Student    │  │  Server       │  │
│  │   - Create      │  │   Interface  │  │  Actions      │  │
│  │   - Review      │  │   - Submit   │  │  - Grading    │  │
│  │   - Analytics   │  │   - View     │  │  - OCR        │  │
│  └─────────────────┘  └──────────────┘  └───────────────┘  │
└─────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────┐
│                    In-Memory Database                       │
│  Courses │ Assignments │ Submissions │ Rubrics │ Students   │
└─────────────────────────────────────────────────────────────┘
                              │
          ┌───────────────────┼───────────────────┐
          ▼                   ▼                   ▼
┌──────────────────┐  ┌─────────────┐  ┌──────────────────┐
│   Claude API     │  │ OCR Services│  │   Redis Cache    │
│   - Grading      │  │ - PaddleOCR │  │   - Results      │
│   - Extraction   │  │ - Pix2Text  │  │   - Jobs         │
│   - Rubrics      │  │ - Surya     │  │                  │
└──────────────────┘  └─────────────┘  └──────────────────┘

Getting Started

Prerequisites

• Node.js 18+ and npm/yarn/pnpm
• Docker and Docker Compose (for OCR services)
• Anthropic API key

Installation

1. Clone the repository

   git clone <repository-url>
   cd tai

2. Install dependencies

   npm install

3. Set up environment variables

   cp .env.example .env.local

   Add your API keys to .env.local:

   ANTHROPIC_API_KEY=your_anthropic_api_key
   # Optional: Other OCR service keys
   MATHPIX_APP_ID=your_mathpix_app_id
   MATHPIX_APP_KEY=your_mathpix_app_key

4. Start OCR services (optional but recommended)

   cd ocr-services
   docker-compose up -d

   This starts:

   • PaddleOCR service on port 8001
   • Pix2Text service on port 8002
   • Surya service on port 8003
   • Redis on port 6379

5. Run the development server

   npm run dev

6. Open the application Navigate to http://localhost:3000

Usage

For Teaching Assistants / Instructors

1. Create Assignment

   • Upload problem set PDF
   • AI extracts rubric or create custom rubric
   • Set point values and grading criteria

2. Add Solution Key

   • Upload solution PDF
   • AI extracts answers
   • Review and confirm

3. Review Submissions

   • View auto-graded submissions
   • Adjust grades if needed
   • Release grades to students

For Students

1. View Assignments

   • See all available assignments
   • Check due dates and requirements

2. Submit Work

   • Upload PDF of handwritten work
   • Wait for automatic grading

3. View Results

   • See total score
   • Review per-question feedback
   • Understand mistakes

Project Structure

tai/
├── src/
│   ├── app/                    # Next.js App Router
│   │   ├── page.tsx           # Main application page
│   │   ├── actions.ts         # Server actions
│   │   ├── api/               # API routes
│   │   ├── assignment/        # Assignment pages
│   │   └── dashboard/         # Analytics dashboard
│   ├── components/            # React components
│   │   ├── ui/               # Reusable UI components
│   │   ├── assignment-*.tsx  # Assignment-related
│   │   └── student-*.tsx     # Student-related
│   ├── lib/                   # Core business logic
│   │   ├── database.ts       # In-memory database
│   │   ├── queries.ts        # Data access layer
│   │   ├── llm.ts            # Claude integration
│   │   ├── ocr/              # OCR engine integration
│   │   │   ├── engines/      # Individual engines
│   │   │   ├── consensus/    # Multi-engine consensus
│   │   │   └── preprocessing/# PDF processing
│   │   └── workflows/        # End-to-end pipelines
│   └── types/                # TypeScript definitions
├── ocr-services/             # Docker services
│   ├── paddleocr-service/
│   ├── pix2text-service/
│   └── surya-service/
├── python/                   # Python utilities
└── scripts/                  # Debug/test scripts

OCR Engines

TAI supports multiple OCR engines for maximum accuracy:

Engine	Best For	Speed	Accuracy
Claude	General text, math notation	Fast	High
PaddleOCR	Printed text	Very Fast	Medium
Pix2Text	Math formulas	Medium	High
Surya	Handwriting	Slow	High
Mathpix	Complex math	Fast	Very High

The system uses consensus algorithms to combine results from multiple engines for better accuracy.

Grading Workflow

1. Student uploads PDF submission
   ↓
2. Multi-engine OCR extraction
   ↓
3. Parse into structured answers (per question)
   ↓
4. Claude AI grades each question
   - Compares to solution key
   - Applies rubric criteria
   - Generates feedback
   ↓
5. TA reviews auto-graded submission
   ↓
6. Release grades to student
   ↓
7. Student views score + feedback

API Routes

Endpoint	Method	Purpose
/api/extract-answer-key	POST	Extract solution key from PDF
/api/extract-submission	POST	Extract student answers
/api/extraction-status/:jobId	GET	Check extraction job status
/api/ocr/multi-scan	POST	Run multi-engine OCR
/api/test-grading-pipeline	POST	Test grading system
/api/sync/assignment	POST	Sync assignment data
/api/sync/submission	POST	Sync submission data
/api/sync/rubric	POST	Sync rubric data
/api/submission-detail	GET	Get submission details

Configuration

Environment Variables

• ANTHROPIC_API_KEY - Required for Claude AI grading
• MATHPIX_APP_ID - Optional for Mathpix OCR
• MATHPIX_APP_KEY - Optional for Mathpix OCR
• NEXT_PUBLIC_* - Client-side environment variables

File Upload Limits

• Max PDF size: 10MB
• Configurable in next.config.ts

Development

Running Tests

# No tests yet - see REFACTORING_PLAN.md
npm run test

Type Checking

npm run build

Linting

npm run lint

Known Limitations

Current Version (Prototype/MVP):

• In-memory database (data lost on restart)
• No authentication/authorization
• No persistent file storage
• No landing page or onboarding
• Limited error handling
• No test coverage

See REFACTORING_PLAN.md for production roadmap.

Technology Choices

• Next.js 16: App Router for modern React patterns, Server Actions for type-safe APIs
• Claude Sonnet 4.5: State-of-the-art reasoning for accurate grading
• Docker: Isolated OCR services for reliability
• TypeScript: Type safety across entire stack
• Radix UI: Accessible, composable components

Contributing

See issues and planned improvements in REFACTORING_PLAN.md.

License

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Contact

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