Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging

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1. Workflow Overview

How It Works This workflow streamlines academic assessment through a multi agent AI system that interprets rubrics, grades submissions, checks for plagiarism, performs quality moderation, generates...

Best for

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

Tools used

n8n-nodes-base.manualtrigger, n8n-nodes-base.set, n8n-nodes-base.httprequest, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.outputparserstructured, n8n-nodes-base.switch, n8n-nodes-base.if

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: Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging
Workflow name: Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging

How It Works

This workflow streamlines academic assessment through a multi-agent AI system that interprets rubrics, grades submissions, checks for plagiarism, performs quality moderation, generates feedback, and escalates borderline cases. Designed for educators and assessment administrators, it reduces inconsistencies in manual marking while embedding integrity checks into every evaluation cycle. A manual trigger retrieves student answers and rubrics, which are first structured before being sent to a Primary Marker Agent. If integrity concerns arise, a Plagiarism Analysis Agent runs in parallel. Results are consolidated and reviewed by a Quality Moderator Agent, followed by a Feedback Generator. Borderline cases are routed to a Secondary Marker Agent, while approved outcomes proceed to escalation preparation, Slack notifications, statistics computation, final consolidation, and logging in Google Sheets.

Setup Steps

Configure manual trigger and connect student answer and rubric data sources.
Add OpenAI API credentials to all OpenAI Chat Model nodes.
Define moderation thresholds in the Route by Moderation Decision rules node.
Configure Slack credentials and set escalation alert channel.
Set plagiarism sensitivity thresholds in the Plagiarism Analyser Agent node.

Prerequisites

Google Sheets with service account credentials
Student answer and rubric data source (API or spreadsheet)

Use Cases

Automated essay and short-answer marking for university assessments

Customization

Replace OpenAI with Anthropic Claude for marking and moderation agents

Benefits

Automates end-to-end marking with built-in plagiarism and moderation checks

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 - Manual Trigger

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 2 - Workflow Configuration

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

Block 3 - Retrieve Student Answer and Rubric

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

Block 4 - Rubric Interpreter Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 5 - Primary Marker Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 6 - Quality Moderator Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 7 - Feedback Generator Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 8 - Final Output Compilation

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

Block 9 - OpenAI Chat Model - Rubric

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

Block 10 - OpenAI Chat Model - Marker

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

Block 11 - OpenAI Chat Model - Moderator

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

Block 12 - OpenAI Chat Model - Feedback

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

Block 13 - Structured Output Parser - Rubric

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

Block 14 - Structured Output Parser - Marker

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

Block 15 - Structured Output Parser - Moderator

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

Block 16 - Structured Output Parser - Feedback

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

Block 17 - Route by Moderation Decision

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

Block 18 - Check Integrity Flags

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

Block 19 - Calculate Statistics

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

Block 20 - Secondary Marker Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 21 - Plagiarism Analyzer Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 22 - OpenAI Chat Model - Secondary

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

Block 23 - OpenAI Chat Model - Plagiarism

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

Block 24 - Structured Output Parser - Secondary

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

Showing the first 24 of 39 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging
Complexity	advanced
Nodes	39
Categories	Document Extraction, AI Summarization
Author	Cheng Siong Chin
Published	25 Feb 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/13710/13710.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 Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging 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.manualtrigger n8n-nodes-base.set n8n-nodes-base.httprequest @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.outputparserstructured n8n-nodes-base.switch n8n-nodes-base.if

Complexity

advanced

Published 25 Feb 2026

Likes 0

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Install path: /data/workflows/13710/13710.json

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Grade exam answers with GPT‐4.1 mini, plagiarism checks, Slack alerts and Google Sheets logging

Workflow preview

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 - Manual Trigger

Block 2 - Workflow Configuration

Block 3 - Retrieve Student Answer and Rubric

Block 4 - Rubric Interpreter Agent

Block 5 - Primary Marker Agent

Block 6 - Quality Moderator Agent

Block 7 - Feedback Generator Agent

Block 8 - Final Output Compilation

Block 9 - OpenAI Chat Model - Rubric

Block 10 - OpenAI Chat Model - Marker

Block 11 - OpenAI Chat Model - Moderator

Block 12 - OpenAI Chat Model - Feedback

Block 13 - Structured Output Parser - Rubric

Block 14 - Structured Output Parser - Marker

Block 15 - Structured Output Parser - Moderator

Block 16 - Structured Output Parser - Feedback

Block 17 - Route by Moderation Decision

Block 18 - Check Integrity Flags

Block 19 - Calculate Statistics

Block 20 - Secondary Marker Agent

Block 21 - Plagiarism Analyzer Agent

Block 22 - OpenAI Chat Model - Secondary

Block 23 - OpenAI Chat Model - Plagiarism

Block 24 - Structured Output Parser - Secondary

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