Course Recommendation System for Surveys with Data Tables and GPT-4.1-Mini

Use the n8n Data Tables feature to store, retrieve, and analyze survey results — then let OpenAI automatically recommend the most relevant course for each respondent.

🧠 What this workflow does This workflow demonstrates how to use n8n’s built-in Data Tables to create an internal recommendation system powered by AI.

It: Collects survey responses through a Form Trigger
Saves responses to a Data Table called Survey Responses
Fetches a list of available courses from another Data Table called Courses
Passes both Data Tables into an OpenAI Chat Agent, which selects the most relevant course
Returns a structured recommendation with: course: the course title
reasoning: why it was selected

> Trigger: Form submission (manual or public link)

👥 Who it’s for Perfect for educators, training managers, or anyone wanting to use n8n Data Tables as a lightweight internal database — ideal for AI-driven recommendations, onboarding workflows, or content personalization.

⚙️ How to set it up 1️⃣ Create your n8n Data Tables This workflow uses two Data Tables — both created directly inside n8n.

🧾 Table 1: Survey Responses Columns: Name Q1 — Where did you learn about n8n? Q2 — What is your experience with n8n? Q3 — What kind of automations do you need help with?

To create: Add a Data Table node to your workflow.
From the list, click “Create New Data Table.”
Name it Survey Responses and add the columns above.

📚 Table 2: Courses Columns: Course Description

To create: Add another Data Table node.
Click “Create New Data Table.”
Name it Courses and create the columns above.
Copy course data from this Google Sheet:
👉 https://docs.google.com/spreadsheets/d/1Y0Q0CnqN0w47c5nCpbA1O3sn0mQaKXPhql2Bc1UeiFY/edit?usp=sharing

This Courses Data Table is where you’ll store all available learning paths or programs for the AI to compare against survey inputs.

2️⃣ Connect OpenAI Go to OpenAI Platform
Create an API key
In n8n, open Credentials → OpenAI API and paste your key
The workflow uses the gpt-4.1-mini model via the LangChain integration

🧩 Key Nodes Used | Node | Purpose | n8n Feature | |------|----------|-------------| | Form Trigger | Collect survey responses | Forms | | Data Table (Upsert) | Stores results in Survey Responses | Data Tables | | Data Table (Get) | Retrieves Courses | Data Tables | | Aggregate + Set | Combines and formats table data | Core nodes | | OpenAI Chat Model (LangChain Agent) | Analyzes responses and courses | AI | | Structured Output Parser | Returns structured JSON output | LangChain |

💡 Tips for customization Add more Data Table columns (e.g., email, department, experience years)
Use another Data Table to store AI recommendations or performance results
Modify the Agent system message to customize how AI chooses courses
Send recommendations via Email, Slack, or Google Sheets

🧾 Why Data Tables? This workflow shows how n8n’s Data Tables can act as your internal database: Create and manage tables directly inside n8n
No external integrations needed
Store structured data for AI prompts
Share tables across multiple workflows

All user data and course content are stored securely and natively in n8n Cloud or Self-Hosted environments.

📬 Contact
Need help customizing this (e.g., expanding Data Tables, connecting multiple surveys, or automating follow-ups)?

📧 robert@ynteractive.com
🔗 Robert Breen
🌐 ynteractive.com