Gpt-4.1 mini-powered learning management automation

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

How It Works Automates daily learner engagement monitoring, progress analysis, and personalized feedback delivery for training programs. Target audience: learning and development teams, corporate t...

Best for

Document Extraction automation workflows
AI Summarization automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.scheduletrigger, n8n-nodes-base.set, n8n-nodes-base.httprequest, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.if, n8n-nodes-base.gmail

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheng Siong Chin.

Original n8n.io source

1.1 Workflow description

Title: Gpt-4.1 mini-powered learning management automation
Workflow name: Gpt-4.1 mini-powered learning management automation

How It Works

Automates daily learner engagement monitoring, progress analysis, and personalized feedback delivery for training programs. Target audience: learning and development teams, corporate training managers, and online education platforms scaling instructor workload. Problem solved: manual progress tracking consumes instructor time; AI analysis identifies struggling learners early for intervention. Workflow runs daily checks on learner activity, retrieves course data and progress, analyzes engagement with OpenAI models, evaluates quiz scores, generates performance summaries, sends progress reports to learners, emails instructors on at-risk cases, generates learning paths, and triggers manager notifications.

Setup Steps

Configure daily schedule trigger.
Connect learning management system APIs (LMS).
Set OpenAI keys for progress analysis.
Enable Gmail for multi-recipient notifications.
Map learner risk thresholds and escalation rules.

Prerequisites

LMS platform credentials, OpenAI API key, learner database, email service for notifications, manager contact lists.

Use Cases

Corporate onboarding programs tracking employee progress, online learning platforms identifying struggling students

Customization

Adjust AI analysis criteria for your curriculum. Integrate Slack for instructor alerts.

Benefits

Reduces instructor workload by 70%, identifies at-risk learners 2 weeks early

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Daily Learning Check

Type / Role: n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices: Version 1.3

Block 2 - Workflow Configuration

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 3 - Get Employee Data

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 4 - Assign Training Modules

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 5 - Training Assignment Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 6 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 7 - Save Training Assignments

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 8 - Get Progress Data

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 9 - Analyze Progress

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 10 - Progress Analysis Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 11 - OpenAI Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 12 - Check If Reminder Needed

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 13 - Send Reminder Email

Type / Role: n8n-nodes-base.gmail - gmail
Config choices: Version 2.2

Block 14 - Get Quiz Submissions

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 15 - Evaluate Quiz Submissions

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 16 - Quiz Evaluation Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 17 - OpenAI Chat Model2

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 18 - Save Quiz Results

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 19 - Generate Learning Paths

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3

Block 20 - Learning Path Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 21 - OpenAI Chat Model3

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.3

Block 22 - Save Learning Paths

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.3

Block 23 - Notify Manager

Type / Role: n8n-nodes-base.gmail - gmail
Config choices: Version 2.2

Block 24 - Prepare Manager Report

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Showing the first 24 of 32 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Gpt-4.1 mini-powered learning management automation
Complexity	advanced
Nodes	32
Categories	Document Extraction, AI Summarization
Author	Cheng Siong Chin
Published	16 Dec 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/11864/11864.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI Summarization workflows

Frequently asked questions

What does Gpt-4.1 mini-powered learning management automation do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Summarization use case.

Cheng Siong Chin

203 workflows

Nodes

n8n-nodes-base.scheduletrigger n8n-nodes-base.set n8n-nodes-base.httprequest @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.outputparserstructured @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.if n8n-nodes-base.gmail

Complexity

advanced

Published 16 Dec 2025

Likes 0

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Install path: /data/workflows/11864/11864.json

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Gpt-4.1 mini-powered learning management automation

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How It Works

Setup Steps

Prerequisites

Use Cases

Customization

Benefits

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Daily Learning Check

Block 2 - Workflow Configuration

Block 3 - Get Employee Data

Block 4 - Assign Training Modules

Block 5 - Training Assignment Parser

Block 6 - OpenAI Chat Model

Block 7 - Save Training Assignments

Block 8 - Get Progress Data

Block 9 - Analyze Progress

Block 10 - Progress Analysis Parser

Block 11 - OpenAI Chat Model1

Block 12 - Check If Reminder Needed

Block 13 - Send Reminder Email

Block 14 - Get Quiz Submissions

Block 15 - Evaluate Quiz Submissions

Block 16 - Quiz Evaluation Parser

Block 17 - OpenAI Chat Model2

Block 18 - Save Quiz Results

Block 19 - Generate Learning Paths

Block 20 - Learning Path Parser

Block 21 - OpenAI Chat Model3

Block 22 - Save Learning Paths

Block 23 - Notify Manager

Block 24 - Prepare Manager Report

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions