AI-Powered Automated Assessment and Feedback Agent

An intelligent, agentic AI system designed to significantly reduce teachers' workloads by providing instant assessment and personalized differentiated feedback and follow-on activities for student assignments.

⚡ Workflow At a Glance

• Upload an assignment and student response (file or text)
• Instantly receive detailed, actionable feedback and differentiated individualized follow-on tasks and a grade
• Review, delete, or clear past assessments in the history
• All actions are accessible, error-proof, and demo resilient

🏆 Hackathon Info

This project was developed for the Microsoft Hack Together: AI Agents Hackathon (April 8–30, 2025).

• See the Official Rules
• Status: Hackathon prototype/MVP

🎯 What It Does (Key Features)

• Automated Grading: Instantly grades student submissions (text or file) for any assignment/task.
• Personalized Feedback: Actionable, contextual feedback including grade, strengths, areas for improvement, individualized activity, and teacher suggestion.
• Assessment History: All assessments are saved locally (browser localStorage) for later review and demo resilience.
• History Management: Delete individual assessments or clear all history, with confirmation dialogs for safety.
• Navigation: Seamless navigation between upload and results/history pages.
• Robust Error Handling: Friendly, actionable error messages for upload, AI, or network issues.
• Loading Spinner: Visual feedback while grading is in progress.
• Accessibility: Screen reader-friendly, keyboard-accessible, and color-contrast aware.

⚙️ How It Works

1. Teacher uploads a student submission (file or text) and assignment description.
2. AI (Azure OpenAI) generates instant, individualized feedback and a grade.
3. Results and assessment history are displayed for review, deletion, or clearing.
4. All data is stored locally (no backend required for history/demo).

🚀 Technical Stack

• TypeScript: Ensures reliability, maintainability, and scalability.
• Azure OpenAI: Provides advanced NLP capabilities for nuanced and accurate assessment.
• Azure Cognitive Services: Enhances semantic analysis for precise feedback generation.

📖 Educational Impact

• Reduces hours spent grading and marking.
• Improves quality and consistency of student feedback.
• Allows teachers more time to focus on direct student interaction and lesson planning.

🔮 Future Enhancements

• Integration with major Learning Management Systems (LMS) for streamlined workflow.
• Expansion of supported assignment types and subjects.
• Development of analytics dashboards for deeper insights into class performance.

📽️ Demonstration Video

[Coming soon: View a full demonstration of the agent in action.]

👥 Team

• Josh Creek
  jcreek.co.uk

🛠️ Getting Started

This project uses SvelteKit and TypeScript with pnpm as the package manager.

Prerequisites

• Node.js (v18 or newer recommended)
• pnpm

Installation & Running Locally

Developing

Once you've installed dependencies with pnpm install, start a development server:

pnpm run dev

# or start the server and open the app in a new browser tab
pnpm run dev -- --open

Building

To create a production version:

pnpm run build

You can preview the production build with npm run preview.

🕹️ Real-Time Events: PartyKit Setup

This project uses PartyKit for real-time tool usage event streaming between the frontend and backend.

Running PartyKit Locally

1. Install dependencies for PartyKit:

   cd partykit
   npm install
   

2. Set up your .env file (in the project root):

   VITE_PARTYKIT_BASE_URL=ws://127.0.0.1:1999
   PARTYKIT_BASE_URL=ws://127.0.0.1:1999
   

   These variables are required for both the SvelteKit frontend and backend to connect to your local PartyKit server.
3. Start the PartyKit dev server:

   cd partykit
   npm run dev
   

   The server will be available at ws://127.0.0.1:1999/party/<room>.
4. Start the SvelteKit frontend (in a separate terminal):

   pnpm run dev
   

Deploying PartyKit to Production

1. Update your .env for production:

   VITE_PARTYKIT_BASE_URL=wss://<your-connection-string>.partykit.dev
   PARTYKIT_BASE_URL=wss://<your-connection-string>.partykit.dev
   

2. Deploy PartyKit:

   cd partykit
   npm run deploy
   

   Wait for the domain provisioning to complete.
3. Update your frontend/backend to use the production WebSocket URL (as above).

Troubleshooting

• If you see Invalid URL errors, make sure your environment variables are set and that you have restarted your dev servers after editing .env.
• Always run the PartyKit dev server from the partykit directory.

See also .env.example for sample configuration.

📚 Resources

• Hack Together: AI Agents Hackathon – Introduction & Getting Started
• Hack Together: AI Agents Hackathon – Building Your Agent

📌 License

Licensed under the Business Source License 1.1.
See LICENSE file for details.