# Analyze and modernize university curricula with OpenAI: n8n workflow

> Started by an event in a connected app, and works across in-memory vectors and n8n data table.

Source: https://millionrounds.com/workflow/analyze-and-modernize-university-curricula-with-openai (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [AI assistants](https://millionrounds.com/workflows?category=AI+assistants#library)

# Analyze and modernize university curricula with OpenAI

Started by an event in a connected app, and works across in-memory vectors and n8n data table. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 24 steps: large, and worth reading before you trust it with anything live.

Large Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fanalyze-and-modernize-university-curricula-with-openai)

**24 steps** Large: read it before trusting it with anything live

**0** Branches: places the flow splits

**0** Loops or waits

**2** Services it connects to

## The flow

Starts it 1 Model 16 Logic 2 Service 5

The steps in the order they run, from what starts it.

- 1 **Start Curriculum Analysis** Manual trigger *Starts it*
- 2 **Split Curriculum Text** Text Splitter Recursive Character Text Splitter *Model*
- 3 **Generate Embeddings** Embeddings Open Ai *Model*
- 4 **Query Embeddings** Embeddings Open Ai *Model*
- 5 **Employment Data Query Tool** Data Table Tool *Service*
- 6 **Cognitive Load Calculator** Tool Code *Model*
- 7 **Alignment Agent Model** Lm Chat Open Ai *Model*
- 8 **Alignment Output Parser** Output Parser Structured *Model*
- 9 **Forecast Agent Model** Lm Chat Open Ai *Model*
- 10 **Forecast Output Parser** Output Parser Structured *Model*
- 11 **Supervisor Model** Lm Chat Open Ai *Model*
- 12 **Supervisor Output Parser** Output Parser Structured *Model*
- 13 **Load Graduate Employment Data** Data Table *Service*
- 14 **Load Enrollment Patterns** Data Table *Service*
- 15 **Extract Course Syllabi** Extract From File *Service*
- 16 **Load Curriculum Documents** Document Default Data Loader *Model*
- 17 **Curriculum Knowledge Base** Vector Store In Memory *Model*
- 18 **Semantic Retrieval Tool** Vector Store In Memory *Model*
- 19 **Industry Demand Forecast Agent** Agent Tool *Model*
- 20 **Learning Outcome Alignment Agent** Agent Tool *Model*
- 21 **Curriculum Modernization Supervisor** Agent *Model*
- 22 **Combine Data Sources** Merge *Logic*
- 23 **Prepare Results for Storage** Set *Logic*
- 24 **Store Analysis Results** Data Table *Service*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 24
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** in-memory vectors, n8n data table
- **Model:** OpenAI
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, plus your own in-memory vectors account, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on an event in a connected app.

Does it use AI?

Yes. It calls OpenAI. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Take your time. At 24 steps it is large: read it before trusting it with anything live.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 24 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fanalyze-and-modernize-university-curricula-with-openai)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

## Connects to

Each opens the other workflows that use it.

[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)[n8n data table](https://millionrounds.com/workflows?service=n8n+data+table#library)

## Workflows like it

Also ai assistants, with the same services where possible, closest in size first.

- [**Detect multi-modal plagiarism with OpenAI and Whisper** 25 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/detect-multi-modal-plagiarism-with-openai-and-whisper)
- [**Segment players and predict churn with OpenAI** 23 steps · calls a model · in-memory vectors, n8n data table](https://millionrounds.com/workflow/segment-players-and-predict-churn-with-openai)
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- [**Draft dental denial appeal packets from Gmail EOBs with OpenAI** 22 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/draft-dental-denial-appeal-packets-from-gmail-eobs-with-open)
- [**Email-to-task automation with Outlook and Microsoft To Do** 26 steps · calls a model · Gmail, Google Tasks](https://millionrounds.com/workflow/email-to-task-automation-with-outlook-and-microsoft-to-do)