Alphaca

Full-stack platform for learning Gen Alpha language, culture, and digital behavior.

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Recognition

Alphaca received the UBS-SMU-X Collaborative Software Development Outstanding Project Award 🏆, awarded to the top team in the cohort for AY 25/26 Semester 2.

A huge thank you to Prof Christoph and Eng Kit for their guidance and support throughout the development of Alphaca.

What This Project Does

Alphaca is a full-stack platform for learning Gen Alpha language, culture, and digital behavior. It combines structured learning with interactive community and creator features so users can explore internet slang, course content, games, and platform-generated content in one place.

Main product areas include:

• Courses and lesson-based learning
• Glossary and term discovery
• Community/blog content
• Games and quiz battles
• Analytics and progress tracking
• Creator tooling for submissions
• Admin moderation and review flows

How AI Is Used In Alphaca

Alphaca uses AI in a few targeted parts of the product rather than treating AI as the whole product.

• Alpha Speak translation converts user text into Gen Alpha style language using glossary-aware context so the results stay grounded in the platform's slang data.
• Blog content can be converted into Alpha Speak through a dedicated backend flow that preserves glossary-linked terms while rewriting the surrounding content.
• Creator course submissions go through AI-assisted moderation and Gen Alpha relevance scoring to help reviewers assess whether submitted content is safe, educational, and culturally aligned with the platform.
• Glossary evaluation uses Gemini-based scoring to support moderation and quality checks on glossary content.
• The Alpha Speak pipeline also includes embedding and vector-search support so glossary context can be selected semantically before prompts are sent to the configured LLM provider.

In practice, the backend owns the AI workflows, prompt assembly, provider integration, and moderation logic, while the frontend exposes those capabilities through the converter, blog, glossary, and creator experiences.

How To Run The Project

Run the backend first, then run the frontend.

Backend

Linux/Macos:

cd Backend
./run-backend.sh

Windows:

cd Backend
.\run-backend.ps1

Frontend

cd Frontend
npm install
npm run dev

The exact environment files, prerequisites, and deeper setup steps are documented in:

• Backend/README.md
• Frontend/README.md

Repository Structure

This repository is organized as a monorepo with the frontend and backend kept in separate top-level directories.

• Backend/ Spring Boot application that exposes the API, handles business logic, manages persistence, and contains the backend test suite and coverage checks. For backend setup and run details, see Backend/README.md.
• Frontend/ Vue 3 + Vite application that provides the user interface, dashboard flows, games, course views, glossary experience, and frontend test/build tooling. For frontend setup and run details, see Frontend/README.md.

In practice, the backend is responsible for data access, authentication enforcement, moderation workflows, and API responses, while the frontend is responsible for rendering the product experience and consuming those APIs during development and runtime.

Maintenance and Contributions

This project is maintained by the repository owner(s) and contributors.

Contributors