AI-Shifu provides a scalable, personalized AI-driven teaching and learning platform designed for creators, instructors, and training teams. It automates the generation and delivery of tailored educational content, aiming to amplify instructor efficiency and enhance learner engagement through one-on-one AI tutoring.

How It Works

AI-Shifu functions as a personalized teaching agent. Users provide expertise and teaching intent, which the AI elaborates into complete learning experiences. It features a personalized explanation engine that adapts tone and content to individual learner profiles, goals, and levels. The system supports interactive Q&A, decomposes complex questions, and suggests next steps. It also enables rapid course assembly from high-level frameworks and integrates across multiple channels for embedding into websites or training portals, reducing repetitive preparation and support overhead.

Quick Start & Requirements

• Primary install/run command: Use Docker Compose. Clone the repository, navigate to ai-shifu/docker, copy .env.example.full to .env, set at least one LLM API key (e.g., OPENAI_API_KEY) in .env, and run docker compose -f docker-compose.latest.yml up -d. A development mode script (dev_in_docker.sh) is available for local code iteration.
• Non-default prerequisites: Docker and Docker Compose are required. An API key for a supported Large Language Model (LLM) is mandatory. Redis is optional but recommended for caching.
• Estimated setup time or resource footprint: Setup is minimal if Docker is installed. Resource usage depends on the LLM and scale.
• Links: AI-Shifu.com (platform demo), docs/i18n.md (i18n guide).

Highlighted Details

• Personalized explanation engine adapts learning paths and tone based on learner background and goals.
• Interactive Q&A engine decomposes questions and suggests follow-up actions.
• Rapid course assembly allows content creation from high-level frameworks.
• Multi-channel integration enables embedding into various platforms.
• Roadmap includes AI script generation, knowledge base integration, and speech I/O.

Maintenance & Community

The project roadmap indicates ongoing development. Specific community channels (e.g., Discord, Slack) or notable contributors/sponsorships are not detailed in the provided README.

Licensing & Compatibility

The README does not specify the project's license. This omission prevents an assessment of compatibility for commercial use or closed-source linking.

Limitations & Caveats

The README does not detail known limitations, bugs, or alpha/beta status. Crucially, the absence of a specified license is a significant adoption blocker, requiring clarification before use. Production deployments necessitate changing default verification codes and securing sensitive configuration values like the secret key.