Lobster Learn

Lobster Learn is an AI-driven learning platform designed to enhance educational experiences through personalized learning paths, automated assistance, and tailored recommendations. The platform aims to support students in various subjects by leveraging advanced AI to adapt to individual learning needs.

Team Members

Name	Email	Role
LEIDWEIN Alex	se23m057@technikum-wien.at	Developer
AL NASOUH Mohammad	se23x501@technikum-wien.at	Designer
BOUTAHAR Lobna	se23m014@technikum-wien.at	Tester
AL AGELE Ismail	se23m017@technikum-wien.at	PM

About the Project

Project Goals:

1. Improve student engagement.
2. Provide personalized and interactive learning experiences.
3. Enhanced Engagement by increasing student interaction through interactive content and user-friendly design.

Functions:

1. Answer student queries.
2. Provide personalized feedback and study recommendations.
3. Facilitate interactive stories and games.

Use Cases:

1. Students receive instant answers to course-related questions.
2. Personalized study plans and performance feedback.
3. AI-generated summaries and exercise descriptions.

Target Audience: The primary target audience includes:

1. Students in educational institutions.
2. Educators looking for tools to enhance classroom engagement.
3. Educational administrators seeking to streamline support services.

Specific Scenarios (Tasks):

1. Personalized feedback and study recommendations.
2. AI-generated quizzes based on uploaded course materials.
3. Interactive decision-based stories or games.

Tools for Development:

PgVector, NodeJS and Angular

Balsamiq: For initial low-fidelity mockups.

Axure: For designing high-fidelity prototypes.

User Involvement and Testing:

Conduct usability testing with a small groups.

Gather feedback to refine user experience.

Prototype Fidelity:

Aim for a high-fidelity prototype or a working implementation that demonstrates core functionalities and provides a realistic user experience.

Build and Running

Development

1. Install dependencies with npm run init.
2. Start the database with docker-compose up -d.
3. Copy the config file with cp server/config/config.example.js server/config/config.js.
4. Start the development server with npm start.

Production

Build using Docker

Build the Docker image:

docker buildx build -t lobster-learn .

To run the image look at the example in docker-compose.yml.

Build from Source

1. Install dependencies with npm run init.
2. Build the project with npm run build.
3. Start the database with docker-compose up -d.
4. Create a config file at dist/config.js. An example is provided in config.example.js.
5. Start the development server with node dist/server.js.

Notes for Developers

pgvector is currently not supported by drizzle-kit, generated migrations must be manually updated to use pgvector.