Plot Ark
VerifiedOpen-source platform generating, tracking, and optimizing curricula via AI agents.
What is Plot Ark?
Plot Ark is an open-source agentic platform featuring separate interfaces for instructors and students. It focuses on evidence-based content creation, behavioral tracking, and iterative improvements within a closed feedback loop.
Generation starts with external research queries followed by structured alignment to established learning principles. Tracking captures detailed interaction data for analysis by specialized agents that identify issues and suggest targeted changes, which instructors then review before implementation.
It serves educators seeking structured AI assistance for course design and students wanting personalized learning paths with profile-based agent teams and progress insights.
What you can build with Plot Ark
Curriculum Generation
Instructors leverage research agents to produce modules grounded in taxonomy and cognitive principles before deployment.
Learner Monitoring
Real-time xAPI data feeds into agent pipelines that detect risks and compare cohort performance for timely interventions.
Iterative Optimization
Analytics translate into module edit proposals that instructors preview, approve, and cycle back into future data collection.
Install Plot Ark
docker-compose upgit clone https://github.com/Schlaflied/Plot-Ark
cd Plot-Ark
cp .env.example .env
# Set AI_PROVIDER=openai or AI_PROVIDER=gemini
# Add the corresponding API key + TAVILY_API_KEY
docker compose up --build- 1Clone the GitHub repository to your local machine.
- 2Configure required services including PostgreSQL and Redis.
- 3Set API keys for chosen LLM providers in the environment.
- 4Launch the stack using Docker Compose.
- 5Access the web portals to set up profiles and begin content workflows.
Plot Ark: pros & cons
Pros
- +Fully open-source with transparent multi-agent architecture
- +Strong integration of learning science into generation pipeline
- +Comprehensive tracking and human-in-the-loop refinement
- +Flexible support for many LLM providers and custom models
Cons
- –Setup involves multiple backend services and configuration
- –Relies on external research APIs which may incur costs
- –Optimization cycle requires active instructor review time
Frequently asked questions
It is released under the AGPL v3 license.
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