aichildedu

A microservice-based AI education platform for children that integrates LLMs, image generation, and speech synthesis to provide personalized storybook creation, intelligent conversational learning, and multimedia content generation.

Stars: 162

Visit

AICHILDEDU is a microservice-based AI education platform for children that integrates LLMs, image generation, and speech synthesis to provide personalized storybook creation, intelligent conversational learning, and multimedia content generation. It offers features like personalized story generation, educational quiz creation, multimedia integration, age-appropriate content, multi-language support, user management, parental controls, and asynchronous processing. The platform follows a microservice architecture with components like API Gateway, User Service, Content Service, Learning Service, and AI Services. Technologies used include Python, FastAPI, PostgreSQL, MongoDB, Redis, LangChain, OpenAI GPT models, TensorFlow, PyTorch, Transformers, MinIO, Elasticsearch, Docker, Docker Compose, and JWT-based authentication.

README:

AICHILDEDU

Overview

AICHILDEDU is an innovative educational platform designed specifically for children, leveraging the power of AI to create engaging, personalized, and educational content. The platform uses a microservice architecture to deliver a variety of AI-powered educational experiences, allowing for flexible scaling and feature expansion.

Features

Personalized Story Generation: Create custom educational stories tailored to specific age groups, themes, and educational focuses
Educational Quiz Creation: Generate engaging quizzes and questions that reinforce learning objectives
Multimedia Integration: Combine text, images, voice, and video into comprehensive educational materials
Age-Appropriate Content: Content customization based on age groups and developmental stages
Multi-Language Support: Support for content generation in multiple languages
User Management: Comprehensive user system with parent, teacher, and admin roles
Parental Controls: Robust parental control features to ensure child-appropriate content
Asynchronous Processing: Non-blocking task execution for resource-intensive AI operations

System Architecture

AICHILDEDU follows a microservice architecture with the following key components:

                           ┌─────────────────┐
                           │   API Gateway   │
                           └───────┬─────────┘
                                   │
┌────────┬────────┬────────────────┼────────────────┬────────────────┐
│        │        │                │                │                │
▼        ▼        ▼                ▼                ▼                ▼
┌──────────┐ ┌─────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│   User   │ │ Content │ │ Learning     │ │Recommendation│ │  Analytics   │
│ Service  │ │ Service │ │ Service      │ │   Service    │ │   Service    │
└──────────┘ └─────────┘ └──────────────┘ └──────────────┘ └──────────────┘
                                 ┌──────────────────────────┐
                                 │       AI Services        │
                                 │                          │
                                 │ ┌─────────┐ ┌─────────┐  │
                                 │ │  Text   │ │ Image   │  │
                                 │ │Generator│ │Generator│  │
                                 │ └─────────┘ └─────────┘  │
                                 │ ┌─────────┐ ┌─────────┐  │
                                 │ │  Voice  │ │  Video  │  │
                                 │ │Generator│ │Generator│  │
                                 │ └─────────┘ └─────────┘  │
                                 └──────────────────────────┘

Technologies Used

Backend: Python, FastAPI, Uvicorn
Databases: PostgreSQL, MongoDB, Redis
AI & ML: LangChain, OpenAI GPT models, TensorFlow, PyTorch, Transformers
Storage: MinIO (Object Storage)
Search: Elasticsearch
Containerization: Docker, Docker Compose
Authentication: JWT-based authentication

Getting Started

Prerequisites

Docker and Docker Compose
Python 3.10 or higher (for local development)
OpenAI API key

Installation

Clone the repository:

git clone https://github.com/CHILDEDUAI/aichildedu.git
cd aichildedu

Create an .env file in the root directory with the following variables:

# Database
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
MONGODB_URI=mongodb://mongodb:27017/
MONGODB_DB=aiedu

# Authentication
SECRET_KEY=your_secret_key_change_in_production

# OpenAI
OPENAI_API_KEY=your_openai_api_key

Start the services using Docker Compose:
```
docker-compose up -d
```
The API Gateway will be available at http://localhost:8000

Local Development Setup

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Install dependencies:
```
pip install -r requirements.txt
```

Run the desired service:

uvicorn aichildedu.user_service.main:app --reload --port 8001

Usage Examples

Generating a Story

import requests

api_url = "http://localhost:8000/api/v1/ai/text/story"

story_request = {
    "title": "The Curious Robot",
    "theme": "Technology and Friendship",
    "age_group": "6-8",
    "characters": [
        {
            "name": "Robo",
            "description": "A curious and friendly robot who wants to learn about the world"
        },
        {
            "name": "Mia",
            "description": "A smart girl who loves technology and building things"
        }
    ],
    "educational_focus": "Introduction to robotics and programming concepts",
    "length": "medium",
    "language": "en"
}

response = requests.post(api_url, json=story_request)
task = response.json()

print(f"Story generation task created: {task['task_id']}")
print(f"Check status at: {task['status_check_url']}")

Checking Task Status

import requests

task_id = "task_12345"
status_url = f"http://localhost:8000/api/v1/ai/text/tasks/{task_id}"

response = requests.get(status_url)
status = response.json()

print(f"Task status: {status['status']}")
print(f"Progress: {status['progress']}%")

Retrieving Generated Content

import requests

task_id = "task_12345"
result_url = f"http://localhost:8000/api/v1/ai/text/tasks/{task_id}/result"

response = requests.get(result_url)
story = response.json()

print(f"Story Title: {story['title']}")
print(f"Summary: {story['summary']}")
print("\nContent:")
print(story['content'])

API Documentation

The API documentation is available at:

API Gateway: http://localhost:8000/docs
User Service: http://localhost:8001/docs
Content Service: http://localhost:8002/docs
Learning Service: http://localhost:8003/docs
AI Text Generator: http://localhost:8010/docs
AI Image Generator: http://localhost:8011/docs
AI Voice Generator: http://localhost:8012/docs
AI Video Generator: http://localhost:8013/docs

Service Architecture

User Service

Handles user management, authentication, and authorization. Manages user profiles, child accounts, and parental controls.

Content Service

Manages educational content creation, storage, retrieval, and organization with the following features:

Content Types Management:
- Stories: Interactive educational narratives with character development, themes, and moral lessons
- Quizzes: Customizable question sets with answers, difficulty levels, and scoring mechanisms
- Lessons: Structured learning materials with educational objectives and prerequisites
Content Organization:
- Categories: Hierarchical organization of content with parent-child relationships
- Tags: Flexible content labeling for improved discoverability and filtering
- Collections: User-created or curated sets of content (custom, curriculum, featured)
Content Filtering and Search:
- Age-appropriate filtering based on min/max age ranges
- Difficulty level filtering (beginner, intermediate, advanced)
- Content rating filtering (G, PG, educational)
- Multi-language support
- Full-text search capabilities
Content Metadata:
- Educational value tracking
- Subjects and themes classification
- Reading time estimation
- Word count metrics
User Interaction:
- Content reactions (like, favorite, helpful)
- Content sharing and permissions
- User-specific content collections
Multimedia Asset Management:
- Storage and retrieval of images, audio, and video assets
- Asset association with educational content

Learning Service

Tracks learning progress, personalized learning paths, and educational achievements with the following features:

Learning Path Management:
- Curriculum Design: Structured learning paths based on age groups and subjects
- Prerequisites: Dependency management for learning materials
- Progress Tracking: Real-time monitoring of learning milestones
- Adaptive Learning: Dynamic adjustment of content difficulty based on performance
Assessment and Evaluation:
- Progress Assessment: Regular evaluation of learning outcomes
- Skill Level Analysis: Tracking of subject-specific competencies
- Performance Metrics: Detailed analytics of learning activities
- Achievement System: Gamified rewards and badges for accomplishments
Personalization Features:
- Learning Style Detection: Identification of individual learning preferences
- Content Recommendations: AI-powered suggestions for next learning steps
- Pace Adjustment: Flexible learning speed based on individual progress
- Interest-Based Learning: Content tailoring based on demonstrated interests
Progress Reporting:
- Detailed Analytics: Comprehensive learning progress visualization
- Parent/Teacher Dashboard: Monitoring tools for guardians and educators
- Progress Reports: Regular automated assessment summaries
- Learning Insights: AI-generated recommendations for improvement
Collaborative Learning:
- Peer Learning: Facilitated group learning activities
- Social Interaction: Safe, moderated peer communication
- Group Projects: Collaborative educational tasks
- Shared Achievements: Community learning milestones
Learning Support:
- AI Tutoring: Intelligent assistance for difficult concepts
- Homework Help: Guided problem-solving support
- Review Sessions: Automated review of challenging materials
- Learning Resources: Additional materials for deeper understanding

AI Services

Text Generator

Creates educational stories and quizzes with the following features:

Personalized story generation based on age, theme, and educational focus
Quiz generation with customizable difficulty levels
Asynchronous task processing for better resource management
Template-based content customization

Image Generator

Creates illustrations to accompany educational content:

Story illustration generation
Character visualization
Educational diagrams and charts

Voice Generator

Provides audio narration for educational content:

Story narration with different character voices
Multi-language support
Age-appropriate voice adaptation

Video Generator

Creates educational videos and animations:

Story animations
Educational concept visualizations
Interactive learning content

Development and Contribution

Development Workflow

Fork the repository
Create a feature branch
Implement your changes
Write tests for new features
Submit a pull request

Code Style

This project follows PEP 8 style guidelines. Please ensure your code adheres to these standards before submitting PRs.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

FastAPI
LangChain
OpenAI
All contributors to the open-source libraries used in this project

For Tasks:

Click tags to check more tools for each tasks

create stories generate quizzes manage educational content track learning progress personalize learning paths

For Jobs:

educational content creator ai education platform developer child-focused ai engineer educational technology specialist ai content curator

Alternative AI tools for aichildedu

Similar Open Source Tools

aichildedu

github

: 162

ai-development-patterns

github

: 238

finite-monkey-engine

FiniteMonkey is an advanced vulnerability mining engine powered purely by GPT, requiring no prior knowledge base or fine-tuning. Its effectiveness significantly surpasses most current related research approaches. The tool is task-driven, prompt-driven, and focuses on prompt design, leveraging 'deception' and hallucination as key mechanics. It has helped identify vulnerabilities worth over $60,000 in bounties. The tool requires PostgreSQL database, OpenAI API access, and Python environment for setup. It supports various languages like Solidity, Rust, Python, Move, Cairo, Tact, Func, Java, and Fake Solidity for scanning. FiniteMonkey is best suited for logic vulnerability mining in real projects, not recommended for academic vulnerability testing. GPT-4-turbo is recommended for optimal results with an average scan time of 2-3 hours for medium projects. The tool provides detailed scanning results guide and implementation tips for users.

github

: 305

Dive

Dive is an open-source MCP Host Desktop Application that seamlessly integrates with any LLMs supporting function calling capabilities. It offers universal LLM support, cross-platform compatibility, model context protocol for AI agent integration, OAP cloud integration, dual architecture for optimal performance, multi-language support, advanced API management, granular tool control, custom instructions, auto-update mechanism, and more. Dive provides a user-friendly interface for managing multiple AI models and tools, with recent updates introducing major architecture changes, new features, improvements, and platform availability. Users can easily download and install Dive on Windows, MacOS, and Linux, and set up MCP tools through local servers or OAP cloud services.

github

: 1.6k

astrsk

astrsk is a tool that pushes the boundaries of AI storytelling by offering advanced AI agents, customizable response formatting, and flexible prompt editing for immersive roleplaying experiences. It provides complete AI agent control, a visual flow editor for conversation flows, and ensures 100% local-first data storage. The tool is true cross-platform with support for various AI providers and modern technologies like React, TypeScript, and Tailwind CSS. Coming soon features include cross-device sync, enhanced session customization, and community features.

github

: 106

mcp-memory-service

The MCP Memory Service is a universal memory service designed for AI assistants, providing semantic memory search and persistent storage. It works with various AI applications and offers fast local search using SQLite-vec and global distribution through Cloudflare. The service supports intelligent memory management, universal compatibility with AI tools, flexible storage options, and is production-ready with cross-platform support and secure connections. Users can store and recall memories, search by tags, check system health, and configure the service for Claude Desktop integration and environment variables.

github

: 724

AutoAgents

AutoAgents is a cutting-edge multi-agent framework built in Rust that enables the creation of intelligent, autonomous agents powered by Large Language Models (LLMs) and Ractor. Designed for performance, safety, and scalability. AutoAgents provides a robust foundation for building complex AI systems that can reason, act, and collaborate. With AutoAgents you can create Cloud Native Agents, Edge Native Agents and Hybrid Models as well. It is so extensible that other ML Models can be used to create complex pipelines using Actor Framework.

github

: 65

llamafarm

LlamaFarm is a comprehensive AI framework that empowers users to build powerful AI applications locally, with full control over costs and deployment options. It provides modular components for RAG systems, vector databases, model management, prompt engineering, and fine-tuning. Users can create differentiated AI products without needing extensive ML expertise, using simple CLI commands and YAML configs. The framework supports local-first development, production-ready components, strategy-based configuration, and deployment anywhere from laptops to the cloud.

github

: 115

ToolUniverse

ToolUniverse is a collection of 211 biomedical tools designed for Agentic AI, providing access to biomedical knowledge for solving therapeutic reasoning tasks. The tools cover various aspects of drugs and diseases, linked to trusted sources like US FDA-approved drugs since 1939, Open Targets, and Monarch Initiative.

github

: 218

bifrost

Bifrost is a high-performance AI gateway that unifies access to multiple providers through a single OpenAI-compatible API. It offers features like automatic failover, load balancing, semantic caching, and enterprise-grade functionalities. Users can deploy Bifrost in seconds with zero configuration, benefiting from its core infrastructure, advanced features, enterprise and security capabilities, and developer experience. The repository structure is modular, allowing for maximum flexibility. Bifrost is designed for quick setup, easy configuration, and seamless integration with various AI models and tools.

github

: 615

aegra

Aegra is a self-hosted AI agent backend platform that provides LangGraph power without vendor lock-in. Built with FastAPI + PostgreSQL, it offers complete control over agent orchestration for teams looking to escape vendor lock-in, meet data sovereignty requirements, enable custom deployments, and optimize costs. Aegra is Agent Protocol compliant and perfect for teams seeking a free, self-hosted alternative to LangGraph Platform with zero lock-in, full control, and compatibility with existing LangGraph Client SDK.

github

: 137

paelladoc

PAELLADOC is an intelligent documentation system that uses AI to analyze code repositories and generate comprehensive technical documentation. It offers a modular architecture with MECE principles, interactive documentation process, key features like Orchestrator and Commands, and a focus on context for successful AI programming. The tool aims to streamline documentation creation, code generation, and product management tasks for software development teams, providing a definitive standard for AI-assisted development documentation.

github

: 221

AgentNeo

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.

github

: 5.9k

klavis

Klavis AI is a production-ready solution for managing Multiple Communication Protocol (MCP) servers. It offers self-hosted solutions and a hosted service with enterprise OAuth support. With Klavis AI, users can easily deploy and manage over 50 MCP servers for various services like GitHub, Gmail, Google Sheets, YouTube, Slack, and more. The tool provides instant access to MCP servers, seamless authentication, and integration with AI frameworks, making it ideal for individuals and businesses looking to streamline their communication and data management workflows.

github

: 4.2k

claude-007-agents

Claude Code Agents is an open-source AI agent system designed to enhance development workflows by providing specialized AI agents for orchestration, resilience engineering, and organizational memory. These agents offer specialized expertise across technologies, AI system with organizational memory, and an agent orchestration system. The system includes features such as engineering excellence by design, advanced orchestration system, Task Master integration, live MCP integrations, professional-grade workflows, and organizational intelligence. It is suitable for solo developers, small teams, enterprise teams, and open-source projects. The system requires a one-time bootstrap setup for each project to analyze the tech stack, select optimal agents, create configuration files, set up Task Master integration, and validate system readiness.

github

: 159

trpc-agent-go

A powerful Go framework for building intelligent agent systems with large language models (LLMs), hierarchical planners, memory, telemetry, and a rich tool ecosystem. tRPC-Agent-Go enables the creation of autonomous or semi-autonomous agents that reason, call tools, collaborate with sub-agents, and maintain long-term state. The framework provides detailed documentation, examples, and tools for accelerating the development of AI applications.

github

: 122

For similar tasks

EasyNovelAssistant

EasyNovelAssistant is a simple novel generation assistant powered by a lightweight and uncensored Japanese local LLM 'LightChatAssistant-TypeB'. It allows for perpetual generation with 'Generate forever' feature, stacking up lucky gacha draws. It also supports text-to-speech. Users can directly utilize KoboldCpp and Style-Bert-VITS2 internally or use EasySdxlWebUi to generate images while using the tool. The tool is designed for local novel generation with a focus on ease of use and flexibility.

github

: 92

tock

Tock is an open conversational AI platform for building bots. It offers a natural language processing open source stack compatible with various tools, a user interface for building stories and analytics, a conversational DSL for different programming languages, built-in connectors for text/voice channels, toolkits for custom web/mobile integration, and the ability to deploy anywhere in the cloud or on-premise with Docker.

github

: 586

StoryToolKit

StoryToolkitAI is a film editing tool that utilizes AI to transcribe, index scenes, search through footage, and create stories. It offers features such as automatic transcription, translation, story creation, speaker detection, project file management, and more. The tool works locally on your machine and integrates with DaVinci Resolve Studio 18. It aims to streamline the editing process by leveraging AI capabilities and enhancing user efficiency.

github

: 377

StoryToolkitAI

StoryToolkitAI is a film editing tool that utilizes AI to transcribe, index scenes, search through footage, and create stories. It offers features like full video indexing, automatic transcriptions and translations, compatibility with OpenAI GPT and ollama, story editor for screenplay writing, speaker detection, project file management, and more. It integrates with DaVinci Resolve Studio 18 and offers planned features like automatic topic classification and integration with other AI tools. The tool is developed by Octavian Mot and is actively being updated with new features based on user needs and feedback.

github

: 777

aichildedu

github

: 162

Jailbreaks

Jailbreaks is a repository dedicated to organizing and curating models suitable for NSFW writing. It serves as a collection of resources for writers looking to explore adult content in a structured manner.

github

: 584

narratrix

NarratrixAI is an AI-powered tabletop roleplaying platform that leverages AI to create dynamic, responsive, and immersive storytelling experiences. It allows users to create their own stories, use it as character chat, or as a full tabletop RPG experience. The platform features a powerful chat system, flexible AI integration, rich character management, powerful storytelling tools, and developer-friendly customization options. Narratrix supports various AI providers through a manifest system and is built with Tauri for native performance across Windows, macOS, and Linux platforms.

github

: 53

doc2plan

doc2plan is a browser-based application that helps users create personalized learning plans by extracting content from documents. It features a Creator for manual or AI-assisted plan construction and a Viewer for interactive plan navigation. Users can extract chapters, key topics, generate quizzes, and track progress. The application includes AI-driven content extraction, quiz generation, progress tracking, plan import/export, assistant management, customizable settings, viewer chat with text-to-speech and speech-to-text support, and integration with various Retrieval-Augmented Generation (RAG) models. It aims to simplify the creation of comprehensive learning modules tailored to individual needs.

github

: 138

For similar jobs

aichildedu

github

: 162

AMchat

AMchat is a large language model that integrates advanced math concepts, exercises, and solutions. The model is based on the InternLM2-Math-7B model and is specifically designed to answer advanced math problems. It provides a comprehensive dataset that combines Math and advanced math exercises and solutions. Users can download the model from ModelScope or OpenXLab, deploy it locally or using Docker, and even retrain it using XTuner for fine-tuning. The tool also supports LMDeploy for quantization, OpenCompass for evaluation, and various other features for model deployment and evaluation. The project contributors have provided detailed documentation and guides for users to utilize the tool effectively.

github

: 153

duolingo-clone

Lingo is an interactive platform for language learning that provides a modern UI/UX experience. It offers features like courses, quests, and a shop for users to engage with. The tech stack includes React JS, Next JS, Typescript, Tailwind CSS, Vercel, and Postgresql. Users can contribute to the project by submitting changes via pull requests. The platform utilizes resources from CodeWithAntonio, Kenney Assets, Freesound, Elevenlabs AI, and Flagpack. Key dependencies include @clerk/nextjs, @neondatabase/serverless, @radix-ui/react-avatar, and more. Users can follow the project creator on GitHub and Twitter, as well as subscribe to their YouTube channel for updates. To learn more about Next.js, users can refer to the Next.js documentation and interactive tutorial.

github

: 104

Verbiverse

Verbiverse is a tool that uses a large language model to assist in reading PDFs and watching videos, aimed at improving language proficiency. It provides a more convenient and efficient way to use large models through predefined prompts, designed for those looking to enhance their language skills. The tool analyzes unfamiliar words and sentences in foreign language PDFs or video subtitles, providing better contextual understanding compared to traditional dictionary translations or ambiguous meanings. It offers features such as automatic loading of subtitles, word analysis by clicking or double-clicking, and a word database for collecting words. Users can run the tool on Windows x86_64 or ubuntu_22.04 x86_64 platforms by downloading the precompiled packages or by cloning the source code and setting up a virtual environment with Python. It is recommended to use a local model or smaller PDF files for testing due to potential token consumption issues with large files.

github

: 96

AnnA_Anki_neuronal_Appendix

AnnA is a Python script designed to create filtered decks in optimal review order for Anki flashcards. It uses Machine Learning / AI to ensure semantically linked cards are reviewed far apart. The script helps users manage their daily reviews by creating special filtered decks that prioritize reviewing cards that are most different from the rest. It also allows users to reduce the number of daily reviews while increasing retention and automatically identifies semantic neighbors for each note.

github

: 59

EngAce

EngAce is a cutting-edge, generative AI-powered application revolutionizing Vietnamese English learning. It offers personalized learning experiences combining AI with comprehensive features. The repository contains source code, documentation, and resources for the app.

github

: 82

TheoremExplainAgent

TheoremExplainAgent is an AI system that generates long-form Manim videos to visually explain theorems, proving its deep understanding while uncovering reasoning flaws that text alone often hides. The codebase for the paper 'TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding' is available in this repository. It provides a tool for creating multimodal explanations for theorem understanding using AI technology.

github

: 966

vocabulary-book-by-deepseek

Vocabulary Book by DeepSeek is a manual for CET-4, postgraduate entrance examination, and TOEFL vocabulary, providing word meanings, roots, example sentences, mnemonic aids, and mnemonic images. The project uses Cline + DeepSeek-R1-16b for over 80% of the code to automatically encode the vocabulary manual. The generated manual includes vocabulary from A to Z for CET-4, CET-6, postgraduate entrance examination, and TOEFL, along with features to generate Anki cards and PDFs. The tool also allows for the creation of mnemonic images for each word and articles.

github

: 355