online assignment plagiarism checker project using ai

Leading AI-plagiarism detection software.

Fictitious.AI provides industry-leading technology for detecting AI-written text in academic assignments. Built to protect academic integrity.

Next-gen plagiarism detection: AI-written content is the newest form of plagiarism, and flies under the radar of existing plagiarism checkers like TurnItIn. Our software detects outputs from ChatGPT, GPT-4, and other generative text models.

Authoritative accuracy.

We use industry-leading ML models to detect generated content with 99.9% accuracy. Our platform takes the guesswork out of the academic integrity protection process.

Seamless integrations

Our integrations with leading LMS systems including Canvas allow you to import students, assignments, and submissions with a single click. Then, you can view results from our interactive console.

Comprehensive detection

Our proprietary system flags content written by generative AI models including ChatGPT, GPT-4, and more. Our industry-leading ML approach is designed to be generic, and detects over 50 text generation techniques.

Built to protect education. Our easy-to-use platform detects AI-generated content in student submissions and makes it easy for educators to identify academic dishonesty.

Get started, contact us today to schedule a no-obligation intro call. we'll reach out to help discover if fictitiousai can help you..

Plagiarism Detection

AI can Check the Authenticity of submitted students’ work. It can do that by detecting exact word matches, word rearrangements, word substitutions, and hidden symbols. It can also detect changes from active to passive voice,  and synonymization. 

Doing that minimizes the time spent reviewing assignments and research papers, improves the accuracy of detecting plagiarism, and generates less false positives

• Detect 59% of all Cheating Cases
• Labeled Plagiarized Articles (or assignments, papers..etc), along with the original sources they’ve been copied from. Labels could indicate the level of plagiarism like: light, heavy, none

1. Get Inspired

Understand the Use-case under 5 minutes

Video (2.3 minutes)

Introducing PlagiarismCheck.org

AI-powered plagiarism detection platform that is used by educational institutions and businesses around the world, using advanced technology which performs extensive searches in any language across the web.

Article (5 minutes)

How AI And Machine Learning Are Used To Detect Plagiarism Online?

Amira assesses oral reading fluency and analyzes errors to pinpoint strengths and weaknesses. Every time a student and Amira read together, Amira generates diagnostic reports that empower teachers and parents

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Article (6 minutes)

Creating Academic Honesty with AI Technology

Using Plagiarism software will check students' work against not just Wikipedia but 16+ billion web pages. AI leverages deep Search technology so all matches are re-validated allowing teachers to get accurate results.

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Copyleaks Case Studies

Explore how Copyleaks helps identify instances of plagiarism for a variety of businesses and academic institutions

Plagiarism Checker X Testimonials

Find out what different organizations are saying about Plagiarism Checker X

Tech focused

More details on the technical aspects of the use-case

Natural Language Processing For Plagiarism Checker

Natural Language Processing plays a decisive role in establishing a link between human language and computer language. See how the NLP algorithm works to curtail Plagiarism.

Plagiarism Detection Using Artificial Intelligence

This paper proposes a new method implemented in a program, where we utilize a text set to identify the copied part by comparing with some existing multiple files (using a KNN Model)

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Github Repo

Identifying Different Writing Styles in a Document Intrinsically Using Stylometric Analysis

An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.

Plagiarism Detection with 3 Machine Learning models

This repository contains code and associated files for deploying a plagiarism detector using AWS SageMaker. The model examines a text file labeling that file as either plagiarized or not.

Article + Repo

Build a Plagiarism Checker Using Machine Learning

Doing similarity search using the Pinecone SDK to find articles that contain the same or similar content as what's entered by the user. App built using Python 3.9+ and Flask 2.0+. Github Repo here

A deep learning based technique for plagiarism detection: a comparative study

The purpose of this study is to give an overview of different propositions for plagiarism detection based on the deep learning algorithms.

Video (13 minutes)

Coding a Plagiarism Detector in Python

Build your own Plagiarism checker in python language just by using Scikit learn Machine learning library in a few lines of code.

Data Sets you can use to build Demos, POCs, or test Algorithms

Identifying Machine-Paraphrased Plagiarism

200,767 paragraphs (98,282 original, 102,485paraphrased) extracted from 8,024 Wikipedia (English) articles (4,012 original, 4,012 paraphrased using the SpinBot API).

Off-the-Shelf Products using AI for Plagiarism Detection

• Plagiarism Checker X

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Build a Plagiarism Checker Using Machine Learning

Using machine learning, we can build our own plagiarism checker that searches a vast database for stolen content. in this article, we’ll do exactly that..

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Plagiarism is rampant on the internet and in the classroom. With so much content out there, it’s sometimes hard to know when something has been plagiarized. Authors writing blog posts may want to check if someone has stolen their work and posted it elsewhere. Teachers may want to check students’ papers against other scholarly articles for copied work. News outlets may want to check if a content farm has stolen their news articles and claimed the content as its own.

So, how do we guard against plagiarism? Wouldn’t it be nice if we could have software do the heavy lifting for us? Using machine learning, we can build our own plagiarism checker that searches a vast database for stolen content. In this article, we’ll do exactly that.

We’ll build a Python Flask app that uses  Pinecone —a similarity search service—to find possibly plagiarized content.

Demo App Overview

Let’s take a look at the demo app we’ll be building today. Below, you can see a brief animation of the app in action.

The UI features a simple textarea input in which the user can paste the text from an article. When the user clicks the Submit button, this input is used to query a database of articles. Results and their match scores are then displayed to the user. To help reduce the amount of noise, the app also includes a slider input in which the user can specify a similarity threshold to only show extremely strong matches.

As you can see, when original content is used as the search input, the match scores for possibly plagiarized articles are relatively low. However, if we were to copy and paste the text from one of the articles in our database, the results for the plagiarized article come back with a 99.99% match!

So, how did we do it?

In building the app, we start with a dataset of news articles from Kaggle. This dataset contains 143,000 news articles from 15 major publications, but we're just using the first 20,000. (The full dataset that this one is derived from contains over two million articles!)

Next, we clean up the dataset by renaming a couple columns and dropping a few unnecessary ones. Then, we run the articles through an embedding model to create vector embeddings —that's metadata for machine learning algorithms to determine similarities between various inputs. We use the Average Word Embeddings Model . Finally, we insert these vector embeddings into a vector database managed by Pinecone.

With the vector embeddings added to the database and indexed, we’re ready to start finding similar content. When users submit their article text as input, a request is made to an API endpoint that uses Pinecone’s SDK to query the index of vector embeddings. The endpoint returns 10 similar articles that were possibly plagiarized and displays them in the app’s UI. That’s it! Simple enough, right?

If you’d like to try it out for yourself, you can find the code for this app on GitHub . The README contains instructions for how to run the app locally on your own machine.

Demo App Code Walkthrough

We’ve gone through the inner workings of the app, but how did we actually build it? As noted earlier, this is a Python Flask app that utilizes the Pinecone SDK. The HTML uses a template file, and the rest of the frontend is built using static CSS and JS assets. To keep things simple, all of the backend code is found in the app.py file, which we've reproduced in full below:

Let's go over the important parts of the app.py file so that we understand it.

On lines 1-14, we import our app's dependencies. Our app relies on the following:

dotenv for reading environment variables from the .env file

flask for the web application setup

json for working with JSON

os also for getting environment variables

pandas for working with the dataset

pinecone for working with the Pinecone SDK

re for working with regular expressions (RegEx)

requests for making API requests to download our dataset

statistics for some handy stats methods

sentence_transformers for our embedding model

swifter for working with the pandas dataframe

On line 16, we provide some boilerplate code to tell Flask the name of our app.

On lines 18-20, we define some constants that will be used in the app. These include the name of our Pinecone index, the file name of the dataset, and the number of rows to read from the CSV file.

On lines 22-25, our initialize_pinecone method gets our API key from the .env file and uses it to initialize Pinecone.

On lines 27-29, our delete_existing_pinecone_index method searches our Pinecone instance for indexes with the same name as the one we're using ("plagiarism-checker"). If an existing index is found, we delete it.

On lines 31-35, our create_pinecone_index method creates a new index using the name we chose ("plagiarism-checker"), the "cosine" proximity metric, and only one shard.

On lines 37-40, our create_model method uses the sentence_transformers library to work with the Average Word Embeddings Model. We’ll encode our vector embeddings using this model later.

On lines 62-68, our process_file method reads the CSV file and then calls the prepare_data and   upload_items methods on it. Those two methods are described next.

On lines 42-56, our prepare_data method adjusts the dataset by renaming the first “id” column and dropping the “date” column. It then combines the article title with the article content into a single field. We’ll use this combined field when creating the vector embeddings.

On lines 58-60, our upload_items method creates a vector embedding for each article by encoding it using our model. Then, we insert the vector embeddings into the Pinecone index.

On lines 70-74, our map_titles and map_publications methods create some dictionaries of the titles and publication names to make it easier to find articles by their IDs later.

Each of the methods we've described so far is called on lines 95-101 when the backend app is started. This work prepares us for the final step of actually querying the Pinecone index based on user input.

On lines 103-113, we define two routes for our app: one for the home page and one for the API endpoint. The home page serves up the index.html template file along with the JS and CSS assets, and the API endpoint provides the search functionality for querying the Pinecone index.

Finally, on lines 76-93, our query_pinecone method takes the user's article content input, converts it into a vector embedding, and then queries the Pinecone index to find similar articles. This method is called when the /api/search endpoint is hit, which occurs any time the user submits a new search query.

For the visual learners out there, here’s a diagram outlining how the app works:

Example Scenarios

So, putting this all together, what does the user experience look like? Let’s look at three scenarios: original content, an exact copy of plagiarized content, and “patch written” content.

When original content is submitted, the app responds with some possibly related articles, but the match scores are quite low. This is a good sign, as the content is not plagiarized, so we would expect low match scores.

When an exact copy of plagiarized content is submitted, the app responds with a nearly perfect match score for a single article. That’s because the content is identical. Nice find, plagiarism checker!

Now, for the third scenario, we should define what we mean by “patch written” content. Patch writing is a form of plagiarism in which someone copies and pastes stolen content but then attempts to mask the fact that they’ve plagiarized the work by changing some of the words here and there. If a sentence from the original article says, “He was overjoyed to find his lost dog,” someone might patch write the content to instead say, “He was happy to retrieve his missing dog.” This is somewhat different from paraphrasing because the main sentence structure of the content often stays the same throughout the entire plagiarized article.

Here’s the fun part: Our plagiarism checker does really well in identifying “patch written” content too! If you were to copy and paste one of the articles in the database and then change some words here and there, and maybe even delete a few sentences or paragraphs, the match score will still come back as a nearly perfect match! When I attempted this with a copied and pasted article that had a 99.99% match score, the “patch written” content still returned a 99.88% match score after my revisions!

Not too shabby! Our plagiarism checker looks like it’s working well.

Conclusion and Next Steps

We've now created a simple Python app to solve a real-world problem. Imitation may be the highest form of flattery, but no one likes having their work stolen. In a growing world of content, a plagiarism checker like this would be highly useful to authors and teachers alike.

This demo app does have some limitations, as it is just a demo after all. The database of articles loaded into our index only contains 20,000 articles from 15 major news publications. However, there are millions or even billions of articles and blog posts out there. A plagiarism checker like this is only useful if it is checking your input against all the places where your work may have been plagiarized. This app would be better if our index had more articles in it and if we were continuously adding to it.

Regardless, at this point we’ve demonstrated a solid proof of concept. Pinecone, as a managed similarity search service, did the heavy lifting for us when it came to the machine learning aspect. With it, we were able to build a useful application that utilizes natural language processing and semantic search fairly easily, and now we have peace of mind knowing our work isn’t being plagiarized.

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With campuswide digital transformation in mind, rollout to thousands of faculty across disciplines took only two months, far quicker than anticipated.

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Keeping student and instructor needs at the core is key to upholding their rigorous standards of integrity while centering students in their learning.

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Give feedback and grade essays and long-form writing assignments with the tool that fosters writing excellence and academic integrity.

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Check for existing as well as new forms of plagiarism - from text similarity and synonym swapping to contract cheating, AI writing, and AI paraphrasing.

Grade paper-based, bubble sheet, and code assignments faster than ever while giving more robust feedback and gaining valuable insights.

Address potential gaps in curriculum and assessment to prepare students for their careers and meet accreditation requirements.

This robust, comprehensive plagiarism checker fits seamlessly into existing workflows.

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Screen personal statements for potential plagiarism and ensure the highest level of integrity before matriculation.

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Further Reading

The Plagiarism Spectrum 2.0 identifies twelve types of unoriginal work — both traditional forms of plagiarism and emerging trends. Understanding these forms of plagiarism supports the development of original thinking skills and helps students do their best, original work.

An educator guide providing suggestions for how to adapt instruction when faced with student use of AI.

Online Assignment Plagiarism Checker Project using Data Mining

Download project document/synopsis.

Plagiarism is defined as to take or theft some work and present it has one’s own work. This grammar and plagiarism checker system is used to analyze the plagiarism data. Plagiarism affects the education quality of the students and thereby reduce the economic status of the country. Plagiarism is done by paraphrased works and the similarities between keywords and verbatim overlaps, change of sentences from one form to other form, which could be identified using WordNet etc. This plagiarism detector measures the similar text that matches and detects plagiarism. Internet has changed the student’s life and also has changed their learning style. It allows the students to get deeper in the approach towards learning and making their task easier. Many methods are employed in detecting plagiarism. Usually plagiarism detection is done using text mining method. In this plagiarism checker software, user can register with their basic registration details and create a valid login id and password. By using login id and password, students can login into their personal accounts. After that students can upload assignment file, which will further divide into content and reference link. This web application will process the content, visit each reference link, and scan the content of that webpage to match the original content. Also, students can view the history of their previous documents. Students can also check the grammar mistakes on the content.

• The system designed to detect similarity among text documents calculates content similarity among specified documents.
• Checking for code also, that is structure-oriented detection.
• Implements WordNet, this will detect synonyms as well.
• Applying efficient string-matching algorithm, which will further reduce the time and increase the efficiency.
• This system is simple and easy to access.
• System will give accurate results based on the content provided.
• This system will generate results in very less time.

Disadvantages

• Active internet connection required.
• If user uploads an incorrect document, then the result won’t be accurate.

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plagiarism-detection

Here are 80 public repositories matching this topic..., patois / hexraystoolbox.

Hexrays Toolbox - Find code patterns within the Hexrays ctree

• Updated Jun 20, 2023

fyrestone / pycode_similar

A simple plagiarism detection tool for python code

• Updated Jun 5, 2023

JonathanReeve / text-matcher

A simple text reuse detection CLI tool.

• Updated Jul 7, 2023

Hassaan-Elahi / Writing-Styles-Classification-Using-Stylometric-Analysis

✍️ An intelligent system that takes a document and classifies different writing styles within the document using stylometric techniques.

• Updated Feb 7, 2023

abhilampard / Simple-Plagiarism-Checker

Web Application for checking the similarity between query and document using the concept of Cosine Similarity.

• Updated Nov 7, 2022

dimondevceo / turnitinFree

This is a free version of Turnitin. Probably the best tool for IB students.

• Updated May 24, 2022

Wazzabeee / copy-spotter

Make plagiarism detection easier. This script will find similar sentences between given files and highlight them in a side by side comparison.

• Updated May 9, 2024

suyash248 / plagiarism_detection

Plagiarism detection using TF-IDF and cosine similarity algorithms.

• Updated Dec 8, 2022

codeclassroom / PlagCheck

Moss Results scraper with powerful insights & analysis 💡

• Updated Mar 11, 2020

BlaCkinkGJ / catch-me-if-you-can

plagiarism detector

• Updated Apr 12, 2021

harirakul / Plagiarism-Detection

Online tool to check for plagiarism of inputted text.

• Updated Jun 11, 2020

vickyfernandes / SoftPlag

Software Source Code Plagiarism Detection

• Updated Oct 5, 2023

hotelll / Music_Plagiarism_Detection

"Music Plagiarism Detection via Bipartite Graph Matching"

• Updated Dec 1, 2021

OSLL / code-plagiarism

Program for finding plagiarism in the source code written on Python3, C, C++ based on compare AST metadata.

• Updated May 18, 2024

metr0jw / Plagiarism-finder

Code/Report/Image Plagiarism finder

• Updated Apr 8, 2024

StardustDL / codesim

A similarity measurer on two programming assignments on Online Judge.

• Updated Jan 6, 2023

romanglo / multiple-writing-style-detector

This project implements a solution of detecting numerous writing styles in a text.

• Updated Jun 29, 2019

CubasMike / plagiarism_detection_pan2015

Plagiarism Detection Approach for PAN 2015 Text Alignment task

• Updated May 11, 2018

ShreshthSaxena / Text-Mining

Using text mining to build a plagiarism detector based on similarity of documents.

• Updated May 28, 2019

scriptographers / UnPlag

🔎 A Plagiarism Detector for C++, Python and text files

• Updated Dec 12, 2021

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The AI Classroom Hype Is All Wrong, Some Educators Say

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Many educators who have used generative artificial intelligence tools in their work have called the emerging technology a “game changer.”

Some say it’s been especially helpful in reducing the time it takes to do planning or administrative work , such as creating schedules, crafting lesson plans, and writing letters of recommendation for students. Teachers say they work an average of 57 hours a week , but less than half of that time is spent teaching.

“I think the use of AI has streamlined many aspects of teaching and has saved much prep time for teachers,” said a high school fine arts teacher in California in an open-ended response to an EdWeek Research Center survey conducted in March and April.

But amid all the encouragement to try the technology, there are plenty of educators who haven’t tried AI tools and don’t plan to start . These educators are more skeptical of the technology and don’t believe it should be used in K-12.

In open-ended responses to the EdWeek Research Center survey, educators shared their reasoning:

It could degrade critical thinking skills

   ai is not as wonderful as you all make it out to be. how do we expect our next generation to learn to think if all we teach them is how to use ai.

— District-level administrator, Ohio

   AI is driving a wedge between critical thinking and imagination.

— High school foreign language teacher, New Jersey

   AI are machines. They have been trained using stolen data. Students should be learning, questioning, problem-solving, and doing their own work. Teachers should as well. I do not believe AI can ethically be used.

— High school English teacher, Louisiana

   Students should not use AI until they have demonstrated some level of mastery on a subject. Students should not even use a calculator until they can do arithmetic calculations without tools. Problem solving starts in the mind, not on a keypad.

— High school math teacher, Texas

   AI and use of computers in the classroom has diminished everyone's ability to think, learn and reason. It's too easy to punch in a subject and get an immediate answer, which may or may not be correct. How many times have we heard "the computer model says this or that," so therefore that's the end of the discussion. Now I hear AI says this or that. Machines do not and can never have the capabilities of the human mind and the human experience. They can never have the ability to reason. They can never have the ability to rely on "gut instinct," which is correct most of the time. They can never have the ability to say "something just isn't right here." All they can do is look at the data that is fed into them and go from there. And that data is totally dependent on the character of the human or humans feeding it into them.

— District-level administrator, Texas

   I feel AI is used less as a resource and more as a crutch. I was shaken when I found out how many yearbook groups have used AI to write their entire yearbook and make the theme and set the ladder and put it together. We don't like students using AI because it's considered "plagiarism" but yet some teachers use it for everything. I don't mind AI as a brainstorming tool but when you give AI the ability to do all your work is when I have issues with it.

— Middle school teacher, Missouri

The human touch is better

   i have never used ai for anything in my job. i would think we still have to follow through with the actual teaching. ai can't do what i do.

— High school math teacher, Michigan

   While AI is the future, it's more important that teachers know their subject matter, and AI should only be used as a supplement to the teacher's scope of knowledge. To use it beyond that is ineffective as the presentation of the knowledge will be presented with less passion and clarity.

— Middle school physical education teacher, Virginia

   While I believe AI is here to stay, I do not believe that it should be used to simply replace the human aspect of the learning experience. If AI is used by instructors or teachers heavily, then the computer is essentially doing the teachers' jobs for them and the teacher is simply the middle person who repeats what the computer tells them.

— High school career-technical education teacher, Missouri

   AI concerns me in that educators need to know their "stuff" before blindly having AI create lessons, etc., to administer in class. I have tried AI and caught multiple errors in its creation. If I had used what AI created, I would have considered myself unethical in teaching students through that lesson because it contained many errors.

— District-level administrator, Alabama

   Utilizing AI to develop assessments is impersonal. If the general scientific community can acknowledge that generative AI utilizes biased information to create material, why would we rely on these tools to create unbiased assessments?

— High school social studies teacher, Montana

The K-12 system isn’t prepared

   i think that ai is a very dangerous phenomenon for learning and education. it seems like it is thrust upon us and unleashed without adequate preparation to handle the consequences for learning and teaching. i think this should be the number one topic for governments and academic institutions to address immediately..

— High school foreign language teacher, Pennsylvania

   I fear AI is yet another trend that education professionals are running headlong into without sufficient forethought and planning.

— Elementary fine arts teacher, Virginia

   I have never used AI and never will. I think it gives fuel to a fire that we won't be able to control.

— Elementary teacher, North Carolina

Concerns about how it affects their jobs

   last year, i spent a lot of time talking with english teaching colleagues about how to tackle the new problem of ai generated student work. we researched apps to check for plagiarism and ai produced writing and didn't find a good source to help us. this new issue is requiring teachers to rethink the types of assignments we give and the ways we ask students to produce writing in class so we can ensure they are producing original works. it's frustrating and time consuming..

— High school English teacher, Minnesota

   Artificial Intelligence will render my job unnecessary within five years. My students use Grammarly and ChatGPT to write their essays, and they even use it to email their teachers. Commercials show corporations praising their staff for using it to email each other. If humans no longer need to learn how to communicate well in writing—if AI does it for us—then what I have been teaching students for decades is no longer needed. What's more, my students already realize this and are showing it in their attitudes and efforts in writing class.

— Middle school English teacher, Massachusetts

Data analysis for this article was provided by the EdWeek Research Center. Learn more about the center’s work.

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