AI Learn Project: Full-Stack Platform for Studying AI Agent Development

What the Platform Is and Who It Is For

AI Learn Project addresses a problem common to developers who want to learn AI agent development: the available study materials are scattered across blog posts, videos, and framework documentation, with no single place to practice skills, track progress, or find the questions that actually appear in technical interviews.

The platform collects four things in one deployable codebase: a curated learning roadmap with linked resources, an AI-scored practice question bank where the model can evaluate answers and ask follow-up questions, an interview question bank organized by topic and weighted by observed interview frequency, and a progression system with experience points, ranks, and badges to give feedback on learning progress.

The topics in the question bank cover AI general knowledge, agent fundamentals, RAG end-to-end pipelines, vector search, multi-agent systems, and security evaluation. The platform has an online version at ai-studyhub.cn and a local deployment path through QUICK_START.md.

Technology Stack and Service Architecture

The project uses three separate backend services plus a frontend. The Vue 3 frontend handles the chat-style question practice UI, the learning roadmap display, the interview question browser, and the user progress views. It is built with Vite, TypeScript, Pinia, Vue Router, and Element Plus.

The Spring Boot backend (Java 17) handles user authentication with JWT, the question database, interaction records, the progression system, and admin endpoints. It uses Flyway for database migrations against MySQL 8.4 LTS.

The FastAPI AI service (Python 3.11 or later) handles answer scoring, follow-up question generation, and the model service integration. It uses Uvicorn and supports streaming responses.

The three services communicate internally: the Spring Boot backend calls the FastAPI service for scoring and discussion features. The project structure reflects this split:

ai_learn_project
├── ai-learn-web       # Vue 3 frontend
├── ai-learn-backend   # Spring Boot backend
├── ai-service         # FastAPI AI service
├── doc                # Design and requirements documents
└── QUICK_START.md     # Setup and deployment guide

Local Deployment and the Quick Start Path

The README directs developers to QUICK_START.md for the full local deployment walkthrough. The deployment requires MySQL 8.4 LTS, a Java 17 runtime, Python 3.11 or later, Node.js, and an external AI model service endpoint.

The architecture diagram in the README shows the data flow:

- User browser communicates with the Vue 3 frontend. - The frontend calls the Spring Boot backend. - The Spring Boot backend queries MySQL and calls the FastAPI AI service. - The FastAPI AI service routes to either a local rule fallback or an external model API.

The AI service is configurable: developers can point it at any model API that supports streaming responses. The README does not document which specific models the question bank was designed against, so the scoring quality depends on matching the model to the question format.

The project includes a QUICK_START.md with environment variable documentation and a verification checklist. Docker-based setup is referenced in the description but the specific Docker configuration files are in the doc/ and release/ directories.

The AI-Scored Practice Experience

The practice question feature is the platform's core differentiator. A developer opens a question card, types or dictates an answer, and the AI model evaluates it on a scoring rubric. The model can then ask follow-up questions to probe understanding. The platform records the highest score, the most recent score, and the conversation history for each question session.

The progression system awards experience points for completing practice sessions. Points accumulate toward ranks and badges, which appear on a personal progress page alongside a record of practice activity, question type breakdown, and weak-topic analysis.

The interview question bank surfaces questions by topic and weights them by how often each question has appeared in real interviews, giving learners a prioritized study order rather than a flat alphabetical list. Topics include AI agent fundamentals, RAG pipelines, vector retrieval, and multi-agent coordination.

Limitations: Chinese-Language Curriculum and Setup Complexity

The question bank and learning roadmap are in Chinese. The online demo at ai-studyhub.cn is in Chinese. Developers who do not read Chinese will find the content inaccessible even if they can deploy the code. The README is in Chinese.

Local deployment requires four separate runtimes: Java, Python, Node.js, and MySQL. The three services must all be running and correctly configured before the full feature set is available. A developer who just wants to experiment with the AI scoring feature still needs the full stack up.

The last push was on 2026-07-30. No GitHub releases are available; deployment is from source.

AI Learn Project Compared to Standard Coding Practice Platforms

Platforms like LeetCode offer structured practice, progression tracking, and topic organization, but they focus on algorithmic problems rather than AI engineering concepts. LeetCode does not have AI-scored free-response questions, RAG pipeline exercises, or agent coordination problems.

AI Learn Project is narrower in scope but deeper in its specific domain. It covers the exact question types that appear in AI engineering interviews, uses an LLM to evaluate and discuss answers rather than a test harness that checks for a specific output, and includes learning resources specific to LangChain, LangGraph, and vector databases.

The trade-off is that AI Learn Project requires deploying and maintaining three services yourself. LeetCode and similar platforms are zero-setup for the learner. For a team setting up an internal training environment specifically for AI agent development, AI Learn Project provides a ready-made structure. For individual developers who want to practice immediately, the online version at ai-studyhub.cn removes the deployment overhead.

Editorial conclusion

AI Learn Project is the right starting point for a developer who wants a concrete, full-stack codebase demonstrating how to integrate AI scoring, streaming responses, and a multi-tier backend for a learning product. It is not a production learning management system: the interview question bank is built around a specific Chinese-language curriculum, and local deployment requires Java 17, Python 3.11 or later, Node.js, MySQL 8.4, and a working AI model service. Before relying on it as a teaching platform, verify that the model API you configure can produce consistent scores on the types of questions you plan to use. The last push was on 2026-07-30.

Frequently asked questions

Official sources

1. Earth-OL-Player/ai_learn_project on GitHub
2. Issues
3. Project website
4. README