Modernizing a curriculum against employment data

The scenario matches course content against employment and demand data, finds the gaps, and prepares evidence-based recommendations to update the program.

  • Courses are matched against real employment data
  • Gaps between program and market are found automatically
  • Demand forecasts feed into the recommendations
  • The final analysis is saved to a Data table
Default Data LoaderEmbeddings OpenAIData table Tool
Connect Data table and runScriptera

How it works

To keep Default Data Loader and Embeddings OpenAI in sync, connect them with a ready-made scenario — changes flow automatically.

  1. Starts when: Start Curriculum Analysis
  2. Then: Load Graduate Employment Data
  3. Check: Combine Data Sources
  4. Then: Curriculum Knowledge Base
  5. Then: Curriculum Modernization Supervisor
  6. Then: Prepare Results for Storage
  7. Then: Store Analysis Results
  8. Then: Load Enrollment Patterns
  9. Then: Extract Course Syllabi
  10. Then: Load Curriculum Documents
  11. Then: Split Curriculum Text
  12. Then: Generate Embeddings
  13. Then: Semantic Retrieval Tool
  14. Then: Query Embeddings
  15. Then: Employment Data Query Tool
  16. Then: Cognitive Load Calculator
  17. Then: Learning Outcome Alignment Agent
  18. Then: Alignment Agent Model
  19. Then: Alignment Output Parser
  20. Then: Industry Demand Forecast Agent
  21. Then: Forecast Agent Model
  22. Then: Forecast Output Parser
  23. Then: Supervisor Model
  24. Then: Supervisor Output Parser

You can launch this Default Data Loader + Embeddings OpenAI integration in Scriptera: describe the task in plain words — the scenario is built, launched and monitored for you.

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