EduSync
EduSync: Prototyping an AI-powered coding education platform.
Product
Development
~$100k
Raised pre-seed
35 days
From concept to working demo
The challenge
Teaching coding and AI prompting is often overwhelming for beginners, especially younger students. Many existing tools are either too theoretical or too advanced, leaving a gap for engaging, accessible learning.
Our client wanted a platform that could combine data-driven insights with fun, game-like learning to keep students motivated and give teachers clear visibility into progress.
The main challenges were:
Designing a progression system that made coding approachable step by step
Motivating students through gamification (EXP + badges)
Giving teachers actionable insights through AI without overcomplicating the dashboard
Implementing a light RAG pipeline to make teacher queries reliable and accurate
Building a prototype in just a few weeks to validate the idea
Our solution
To test the vision, we built EduSync, an AI-assisted prototype platform that teaches coding concepts and prompting through interactive games like Tic-Tac-Toe and Rock-Paper-Scissors.
The student experience followed a clear progression: starting with quizzes, moving to fill-in-the-blanks, and advancing to full prompts or pseudocode. An AI chatbot provided real-time hints, while video tutorials supported core Python and JavaScript concepts. A built-in gamification engine awarded EXP and badges, turning progress into levels and achievements that kept students motivated.
On the teacher side, we developed a smart AI assistant powered by a LightRAG pipeline. Teachers could ask natural questions like “How is Tommy from Class 3B doing?” and the system would query Supabase tables, process results through RAG, and return accurate, human-readable insights.
Key features
For students:
Multiple interactive coding games
Stepwise progression system (quiz → fill in the blanks → full prompts)
AI chatbot for hints and explanations
Gamification engine with EXP and badges for motivation
Video tutorials for fundamentals
For teachers:
LightRAG-powered Teacher AI assistant for natural queries about student progress
Performance dashboards with visualized data
Automatic reports on student performance and insights, with the option to export to PDF
Tracking of actions like time spent, errors made, hints used
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Tech stack
We chose a stack that enabled rapid prototyping while keeping the door open for scale.
Frontend: Next.js (student and teacher dashboards)
Backend: Python (FastAPI) for progression logic and AI integrations
Database: Supabase for authentication and structured tracking
AI integration: OpenAI API for chatbots and insights
RAG pipeline: LightRAG for query processing and schema understanding
Data visualization: Recharts for performance dashboards
