plagiarism thesis control

Free Online Plagiarism Checker

Possible plagiarism detected!

If you submit this paper, your institution may take disciplinary measures against you. The content requires editing and modification of parts. We know how to make it unique.

This is weighted average of all matches in your text. For example, if half of your paper is 100% plagiarized, your score would be 50%

Well done, your text is unique!

Need an essay written but don't have the time?

With PapersOwl you’ll get it professionally researched, written and received right on time!

Make it unique with

Increase your SEO performance with

Text matches these sources

Verifying your text. It’ll take approximately 10 seconds

Get a 100% accurate report from an advanced AI-powered writing assistant. Our plagiarism checker works with all common file formats.

• Deep Search
• Check in real time
• Data Safety

How to avoid plagiarism?

Proper citation style.

Avoid plagiarism by always listing the source and formatting it correctly when you are note-taking. Take care of the proper formatting and citation style when using content from outside sources.

Write on your own

Avoid borrowing and overusing large pieces of the content from outside sources, especially from Wikipedia. Write your own thoughts and use sources only to support your opinion (remember to cite it though!).

Rewriting Service

PapersOwl expert can rewrite up to 75% of your content, edit and proofread your paper to make it plagiarism free and ready to use.

Editing Service

PapersOwl expert can edit up to 50% of your content, proofread and polish your paper to make it plagiarism free and ready to use.

Writing Service

PapersOwl expert can rewrite your paper from scratch according to instructions and guidelines and make it plagiarism free and ready to use.

Suits your similarity index. Consider using it!

Plagiarism Checker Review

Get speed and uniqueness when you use the free Papersowl plagiarism checker that accepts an unlimited word count compared to other platforms.

Online Plagiarism Checker For Students

Writing an academic paper can be challenging when you’re not sure if it’s original enough to pass a plagiarism check. Of course, students take information from various sites before writing their own text. Sometimes, it just so happens that certain parts are very similar to your resources, making your professor think that you’ve just copied work from somewhere. That’s why it’s crucial for any modern college or university student to ensure that their work has 100% original content to maintain academic integrity.

Luckily, a free plagiarism checker online can solve this issue quickly and easily. Many professional writing services use a plagiarism checker for research paper. However, students sometimes forget that they should too. But with so many options that pop up when you ask Google to “check my paper for plagiarism”, how do you choose the right one for detection? We’ve got the solution in the form of PapersOwl’s free plagiarism checker tool! Our simple tool makes it convenient to check any writing task without having to spend a dime. It works quickly and highly accurately, ensuring that you get the top grade you deserve. So, if you want to check plagiarism online before turning your task in, head over to our website and get started!

Accurate Check for Plagiarism with Percentage

Many students wishing to produce original content aren’t quite sure how to get an exact percentage of plagiarised text in their work. This percentage is important since many universities have a certain limit of non-unique words you can have in your essay for it to be considered okay. If your plagiarism search doesn’t give you the exact percentage, you can’t be sure if your assignment will go through or not.

When using a free plagiarism tool, it’s essential to have this data provided to you. Only when you have it can you decide which parts to change and which ones to chuck out to achieve your desired results. Plagiarized content is a big issue in modern educational institutions, so getting reliable and trustworthy results is vital. This is the most essential requirement when you check plagiarism.

PapersOwl’s plagiarism detection tool gives you all the information you need to fix plagiarized content. Whether you’ve fallen victim to accidental plagiarism or have tried to make your life easier by copying some text from different sources, you’ll get an accurate percentage with our plagiarism checker online. If you’re wondering how to check paper for plagiarism, it’s nothing complicated at all! Simply visit our site, paste your whole essay into the relevant text box or upload the text file, click on Check For Plagiarism, and you’ll get accurate plagiarism results in a matter of seconds. You’ll see the problematic parts with plagiarism detected highlighted, with links to where similar content exists. Our service with plagiarism detector will also give you the option to check my paper for plagiarism and then to hire a professional writer to fix your task quickly if you’re busy with other things!

The Fastest Plagiarism Checker Online

Gaining insight into duplicate content only works if you get your results quickly. There are so many free plagiarism software online that promise to do the job for you. However, a lot of them are clunky, slow, and inaccurate. How can you produce original work without similarity detection you can trust?

PapersOwl stands out in this regard because it will detect plagiarism in seconds. This is a plagiarism scanner that’s able to perform a Swift Check to give you a uniqueness check right there and then. It also conducts a Deep Search, going through millions of sources on the internet to check for plagiarism. A document of about 1500 words takes only about 10 seconds to get processed! You get a clear plagiarism score of how much text is plagiarized and how much is original. All the sources that your essay matches are listed based on how much similarity there is in your academic writing. And on top of that, you get a handy Make It Unique button that’ll take you to an order page where you can ask our expert writers to rewrite your work and make it 100% unique.

All of this is done almost instantly, allowing students to continue working on their assignments without missing a beat. Not every plagiarism detection software works this quickly, making ours the best one you’ll ever use.

Plagiarism Checker Helps Boost Your Grade

A lot of students make the mistake of considering their papers automatically free from plagiarism. After all, they’ve written it themselves, so how could it be problematic? What they don’t realize is that it’s very easy to borrow some information mistakenly. Turning such a paper in can cause multiple problems, as your professor might think you haven’t done the work at all.

That is why you should always use a plagiarism scanner to test for plagiarized content in your college papers. Our online plagiarism checker for students is designed for this exact purpose. A simple, free plagiarism check could help you check plagiarism, fix any mistakes you see, and submit high-quality text that no one will question.

Our plagiarism detector has a lot going for it. It makes plagiarism detection easier than ever before. Unlike copying and pasting each passage individually into Google, simply upload the whole file into our plagiarism checker free for students, and you don’t have to do anything else. All the matches are highlighted so you know what to change.

The plagiarism test will give you a uniqueness percentage too. This will help you figure out where you stand and how much time you need to adjust anything if required. So, using our copyright checker online free to check your writing is essential. This way, you’ll submit the task only when you’re sure it meets the level of uniqueness required by your school. As a result, your grades will drastically improve when you check for plagiarism.

Benefits of Free Plagiarism Checker for Students

Our professional online plagiarism checker work offers too many benefits to ignore. With our plagiarism detector, you can enjoy highly accurate results as a comprehensive report. The plagiarism checker for students is designed to help you achieve 100% uniqueness without hassle. Here are the key advantages you can enjoy when you check plagiarism free with our plagiarism detection tool:

It’s completely free! We know you are on a tight budget and should be able to check your paper for plagiarism without worrying about payments, so we’ve made the best similarity checker free for all!

Our software detects plagiarism swiftly. It’ll show you detailed results in as little as 10 seconds so you can continue working immediately.

The report from our plagiarism tool gives you access to all the links from where it has detected similarities in your work. You can head to the relevant sites and see which information you must rewrite to improve your results.

Our best free plagiarism checker doesn’t require any skills and presents its services in a simple-to-use interface that anyone can use.

The plagiarism test allows you to get professional help with your work if you’re short on time. Simply ask one of our writers to rewrite the problematic parts of your text and enjoy top grades.

With PapersOwl plagiarism detector, there’s no need to search the internet for an accurate tool. We have many satisfied students worldwide who can vouch for our plagiarism-checking services. Give our online plagiarism checker free tries as often as you want and see how easy it is to produce original essays without spending a penny!

Free Tools for Writing

PapersOwl is a well-known provider of all types of academic papers.

• Research paper
• Dissertation

and many more

• Stuck with a lot of homework assignments?
• Worried about making your work 100% plagiarism free?
• Looking for a writing help with affordable price?

How Does Plagiarism Checker Work?

• If you already have a completed text, all you need is just to copy-paste the whole thing in the special box of the chosen plagiarism tool or website, choose suitable settings (if any), then press “check for plagiarism”. It is quite simple and takes just a few moments.
• Once you have pressed “check for plagiarism”, the system will analyze your text and compare it with different sources to find similarities. As a rule, the duration depends on the text’s length. A standard free online plagiarism checker with percentage can give you the result within five minutes or less.
• When the system finishes the work you will be transmitted to the reporting page – it contains the comprehensive report on your work, a percentage of its uniqueness, and a list of sources on which similarities were detected. Often, such tools also highlight the overlaps that were found.

As you can see, it is simple. However, for the best and reliable result you have to be careful. There are tons of programs and online tools that can be used but keep in mind that many of them work differently and not all are good for you. To be confident in the truthfulness of the received result, you need to select the best plagiarism checker because only a professional and high-quality software can detect all similarities and give you a reasoned assessment.

Polish your paper and get rid of plagiarism!

We’ll change up to 75% of your paper, edit and proofread it.

• Reliable Editors
• Any Field of Study
• Fair Prices

Free Plagiarism Checker is rated 5.0 /5 based on 735 user reviews.

Want your voice to count in? Send us your review with all the details.

Advantages Of Plagiarism Checker By PapersOwl

Why choose us? Our service offers a professional online plagiarism checker with report that will provide you with a comprehensive report to make you confident in the 100% uniqueness of your paper. Our free plagiarism checker for students guarantees the best check and here are the key advantages of using our tool that prove this:

You don’t need to pay anything to check your paper for plagiarism because we know the value of original and unique works.

One of the main benefits of our antiplagiat checker online is that it works so fast that you will not even have enough time to make yourself a cup of coffee while it analyzes your text, and it is safe!

We use the latest and the best algorithms and software in order to provide you with an advanced check and help you receive the high-quality papers.

It is simple in use and won’t take much time!

Many students have already confirmed that our free tool is a great and convenient feature that helped them detect and fix errors that could lead to a failure. With us, you will no longer need to look for a different scanner!

Leaving already?

Get 10% off your first order!

* you'll see the discount on checkout page

OUR WRITERS

You can choose the writers after viewing information about them. Just select the writer whose experience is closest to your subject.

Completed orders: 720

Cushing′s Syndrome

• Paper Type: Essay (Any Type)
• Subject: Psychology

Completed orders: 3450

Symbolism in to Kill a Mockingbird

• Subject: Literature

Completed orders: 750

Social media impact

• Subject: English

Plagiarism Checker FAQ

Can i check my essay for plagiarism free online, can i use papersowl plagiarism checker as a student for free, can i check my research paper for plagiarism for free, will the papersowl plagiarism report be the same as at my university, what are the consequences of plagiarism, why wait place an order right now.

Simply fill out the form, click the button, and have no worries!

Trusted Online Plagiarism Checker For Students and Academics

How to Use the Paperpal Plagiarism Checker to Write Responsibly

Check your paper for similarity

Review report for actionable items

Write in your own voice

Cite any overlooked sources

Maintain Academic Integrity with the Best Plagiarism Check Tool

Achieve writing excellence with our secure, reliable plagiarism checker

Paperpal Offers More Than An Online Plagiarism Checker

Paperpal Prime provides comprehensive language correction, paraphrasing, and generative AI writing capabilities to transform your academic writing.

Online Paraphrasing Tool

Use Rewrite to paraphrase content and add variety, achieve an academic tone, and reduce length to achieve clear, concise, impactful academic writing.

Free Grammar Checker

Benefit from in-depth grammar checks that quickly identify and correct spelling, punctuation, and complex language and grammar errors in minutes.

Generative AI Assistant

Access our suite of secure generative AI features designed to help you overcome writer’s block and deliver high-quality, original content, every time.

Frequently Asked Questions: Paperpal’s Plagiarism Checker

How can i access paperpal’s plagiarism check, how many pages or words can i check per month, will my unused quota transfer to the next month, what software is being used to detect similarity, what database is my writing checked against, how do i interpret the similarity score, why did i get a high similarity score after polishing language with paperpal using copilot’s ai-generated text, can paperpal ensure that my content is not flagged by plagiarism or ai detection tools, my paper is completely original. why is my similarity score so high, what types of documents can i upload, are my documents stored and added to turnitin’s database, understand plagiarism and ethical writing.

Want to master the basics of plagiarism and learn how to use AI tools responsibly? Check out these curated articles on plagiarism and ethical academic writing guidelines!

Self-plagiarism in research: What it is and how to avoid it

Self-plagiarism in research should be strictly avoided. Yet, it’s quite common to find early career researchers, and sometimes even experienced scholars...

The Do’s & Don’ts of Using Generative AI Tools Ethically in Academia

Generative AI has ushered in a new era of possibilities, redefining the way we create content, analyze data...

Addressing Your Queries on AI Ethics, Plagiarism, and AI Detection

The integration of artificial intelligence (AI) tools into the academic writing process has sparked numerous debates...

Top 5 ethical considerations in research

While research is based on the pillars of innovation, trust, and transparency, it also requires scientists and academics...

Get Paperpal

Regional Websites

Connect with us

Shape the future

We are always looking for inspiration, feedback, and ideas. With your help we can make Paperpal even more amazing together!

Plagiarism Checker by Quetext

Free plagiarism checker: how it works, enter text into plagiarism detection tool.

We make it simple. Just copy and paste all content from your document into our plagiarism checker and hit the ‘Check Plagiarism’ button to get started.

Evaluate text for plagiarism

Our plagiarism detection tool uses DeepSearch™ Technology to identify any content throughout your document that might be plagiarized. We identify plagiarized content by running the text through three steps:

• 1.) Contextual Analysis
• 2.) Fuzzy Matching
• 3.) Conditional Scoring

Accurate plagiarism results

After evaluating the text against billions of internet sources, you will be provided with a plagiarism score showing the percentage of text that is an exact or near-match to existing text online.

Resolve plagiarism risk and use citations

Our ColorGrade™ feedback feature highlights exact matches vs. near-exact or “fuzzy” matches with corresponding colors. From there, you can resolve plagiarism issues by deleting or altering the at-risk copy. Or, you can use our handy “Cite Source” feature to generate citations in MLA, APA, and Chicago formats and insert the citations directly into your document.

Plagiarism Checker Benefits

Whether producing original content or verifying that of others, there’s a lot to gain from using a plagiarism checker. Accurate, automatic detection of duplicate content facilitates the copy-checking process for teachers, students, content writers, and more. Results showing the exact percentage of plagiarized content allows users to see exactly how much text has been copied and where they need to re-word.

For Teachers

Before homework can be graded for quality, it must first be confirmed as original. Our easy-to-use tool arms teachers with a simple, effective way to verify and grade students’ work. Educators at all levels can benefit from ensuring academic integrity through a comprehensive plagiarism check. From K-12, all the way through higher education, teachers are faced with the task of verifying the originality of the work of dozens, if not hundreds, of students each year. Automating this process frees teachers up to focus on the quality of work, rather than be bogged down by its originality.

For Students

While the prevalence of academic plagiarism is on the rise, much of it is arguably unintentional. A simple, yet accurate and comprehensive, plagiarism checker offers students peace of mind when submitting written content for grading. It is much easier to do a quick check for potential plagiarism before submission rather than convince a teacher after the fact that your academic integrity is not in question. And Quetext even takes checking for plagiarism a step further, helping students identify and cite the source itself with our built-in citation generator.

For Copywriters

Plagiarism risk is not restricted to academia. Anyone tasked with writing for an individual or business has an ethical and legal responsibility to produce original content. On top of that, content writers are often tasked with producing content on topics outside of their wheelhouse, leaving them reliant on the work of others for their research. Our plagiarism checker gives content writers a quick and easy method to prevent copyright infringement. Checking even lengthy pieces of writing takes only a few minutes, keeping companies’ public content in check and writers’ integrity intact.

Types of Plagiarism

It’s important to understand that plagiarism expands far beyond just copying someone else’s work word-for-word. There are several different types of plagiarism that should be avoided.

Self-Plagiarism

Many believe that, as long as they produced the work at some point in the past, they can include it in future pieces. However, even if you were the original author, that original work must be cited in order to not be flagged as plagiarism. Treat your past self as a totally separate author; be sure to include all relevant citations and quotations, the same as you would for any other source.

Patchwork Plagiarism

Patchwork plagiarism is the act of piecing together a "patchwork" of existing content to form something new. Assembling unoriginal content in this manner often involves some paraphrasing, with only slight changes. This type of plagiarism can be tricky and can certainly occur unintentionally, especially in academia. Since academic writing is largely based on the research of others, a well-meaning student can inadvertently end up plagiarizing.

Mosaic Plagiarism

Mosaic plagiarism is synonymous with patchwork plagiarism. It describes the process of loosely rearranging or restating another's work without issuing proper credit. It can occur accidentally or intentionally. For authors, mosaic plagiarism endangers their academic integrity or reputation as a writer. For those checking content originality, such as teachers, mosaic plagiarism can easily appear to be original content, which can make mosaic plagiarism especially difficult to detect manually.

Accidental Plagiarism

Plagiarism doesn’t have to be intentional to still be considered plagiarism — even in early academia, where students are just learning how to properly cite others’ work. While there may be no ill intent from the student, most schools have policies explicitly treating accidental plagiarism the same as intentional plagiarism. Students are expected to know how to properly issue credit to other authors. Similarly, content writers risk damage to their reputation if they produce plagiarized content, regardless of intent.

Plagiarism Checker FAQ

What is plagiarism.

Plagiarism is representing someone else’s work as your own. In educational contexts, there are differing definitions of plagiarism depending on the institution. Plagiarism is considered a violation of academic integrity and a breach of journalistic ethics.

What percentage of a paper can be plagiarized (or copied) and still be considered unique?

Generally speaking, similar or exact copies of another source should be kept under 15% for the total text of the article/paper/essay. As a best practice, citations should be used whenever using another source word-for-word.

What’s the difference between deliberate and accidental plagiarism?

Deliberate plagiarism is purposely copying works from books, articles, webpages, or someone else’s paper and representing it as your original work. Alternatively, accidental plagiarism occurs in a few different ways:

• Incorrectly citing another person’s works
• Failing to paraphrase another person’s works - even when citing it correctly
• Reusing your own previous papers and inadvertently representing it as a new idea

What are the consequences of plagiarism?

The consequences for plagiarizing another person’s works vary. But broadly speaking, the types of consequences can be grouped by person and profession.

Plagiarism consequences for students

Maintaining academic integrity is a top priority for every educational institution. As already mentioned, ignorance of how to properly cite sources is not an excuse for plagiarism. It is the student’s responsibility to ensure they are submitting work that has not been plagiarized.

Failure to do so can result in disciplinary action, including an automatic failed grade, removal from a class, or expulsion from a school or university. Students who are allowed to continue at their institution following an act of plagiarism may encounter mistrust and additional scrutiny from teachers and instructors.

Plagiarism consequences for copywriters

Copywriters stake their reputation (and by extension, that of their client or company) on their writing. All copywriters must produce completely original content for their clients.

The consequences for plagiarism here are clear: Copywriters who plagiarize the content of others will quickly find it difficult to obtain paying assignments. Similar to academic situations, it is the copywriter’s own responsibility to ensure that their content is 100% original.

Plagiarism consequences for journalists

Journalists are held to exceptionally high standards of integrity in their writing. A journalist who produces plagiarized content jeopardizes the trust of their readers and publishers. Plagiarism can instantly reduce a journalist’s career by a large margin. The ethical and legal standards issued to journalists are clear: Produce original, well-cited content or find another field.

Model Heading

Limited access for free users , Free users can select max 3 files at one time, while premium users can select up to 20 files per search Make yourself a premium user

Check Plagiarism

Check-Plagiarism offers a reliable plagiarism checker that is widely used to detect plagiarism. Our plagiarism detector offers deep plagiarism detection for writers as well as students in schools, colleges and universities. To use this tool, enter your content below and click on the button.  

Uploading...

Upload File

Verify, Are You Human?

• Sentence wise results
• Matched Sources
• Save Report Download Report

Rewritten Text

Features of Check-Plagiarism

Our tool helps to check plagiarism in your essays, assignments, and blogs. There are many helpful features we have developed in our tool. Here are some of the main ones:

Fast & Accurate

Our plagiarism checker scans your text against billions of web pages & online resources. You can check up to 1,000 words at once. (For premium users, the word limit is increased to 20,000).  Upgrade to pro in just $7

100% Safe & Secure

All files and documents checked with our free plagiarism checker are 100% safe & secure. Your data is completely secure on our servers. No user/search engine/crawler can access your data in any capacity.  

Multiple files support

You can check copied content in up to five documents at once (supported formats: doc, docx, txt, and pdf). Scanning multiple papers will not decrease the speed of the tool. You can also compare documents for plagiarism between them.

Easy-to-Use UI

Our plagiarism checker has a very easy-to-use interface. It provides a simple process to check for content similarities. You can generate a detailed report with percentages.   

Enhanced Reporting with the percentage

Our plagiarism detector scans your text with online resources and provides a complete report with percentages. It helps you to check which sentences of your content are unique and which are plagiarized.

Sentence based checking

Our tool scans every single sentence and compares it with all online resources to detect plagiarism. It uses advanced AI algorithms to detect paraphrased plagiarism.  

  Relevant Tools

• Paraphrasing Tool
• Article Rewriter
• Word Count & Character Count
• Citation Generator
• Plagiarism Comparison Search
• Grammar Checker
• Text Summarizer

Why Check-Plagiairsm?

Online plagiarism checker users.

Our online plagiarism checker is widely used and loved by thousands of students, teachers, and content writers. We provide super-fast plagiarism detection solutions for colleges, universities, and all other educational institutes.

Students use it to check their papers, assignments and thesis for plagiarism.  It is important for students to check their papers for plagiarism because plagiarism is considered a form of academic dishonesty . This means that if a student submits a paper that includes plagiarized material, they could face serious consequences , such as failing the assignment or the entire course, or even being expelled from school .

Teachers use our plagiarism checker tool to ensure that their students are submitting original work. Our tool can help teachers quickly identify plagiarism in student work, so they can take appropriate action. 

By using our tool teachers can

Ensure academic integrity: By checking for plagiarism, teachers can ensure that their students are submitting original work and not cheating.

Promote original thinking: teachers can encourage their students to think critically and independently to develop their own ideas

Prevent grade inflation: If plagiarized work is undetected it can lead to grade inflation that can damage the integrity of the institution. It is also important to maintain the reputation of the institution. Check-Plagiarism also provides matched sources, so it helps to cite sources in your document. Use our citation generator  to create APA, MLA, and Chicago formats.

Writers use this plagiarism detector:

To ensure originality : Plagiarism checker helps writers to ensure that their work is original and not copied from other sources.

To identify and correct mistakes : Writers can identify and correct any unintentional mistakes, such as failing to properly cite a source.

To improve credibility : Authors can establish their credibility as trustworthy sources of information by making sure their work is original and properly cited.

Webmasters use this tool to make sure their content is plagiarism-free because

Plagiarized content can damage the reputation and credibility of a website.

It's also important because  Google has clear policies on plagiarized content and penalizes websites that contain copied content.

It can lead to legal problems, If the original content is protected by copyright. 

Educational institutes (colleges and universities) can integrate our tool using Plagiarism APIs .

Law firms use this tool to protect the documents of their clients. 

Check more details about how to avoid plagiarism . 

How to use this Plagiarism Checker free?

To check plagiarism of your content by using our plagiarism detector online, follow the below instructions:

Type or paste your text into the input box.

Exclude the URL (if any) from the “Show Advanced” option. This URL will be ignored while checking for plagiarism, any text copied from the excluded URL will be marked as unique content.

Click on the Check Plagiarism button. We have added recaptcha security to avoid robots. 

All done? No extra steps are needed. Our free online plagiarism checker will scan the given content against millions of online resources and generates an accurate plagiarism report with percentage. With Deep-Search technology, free plagiarism checker can also detect instances of plagiarism accurately.

Frequently Asked Questions?

  what is considered plagiarism.

According to Oxford: “Plagiarism is presenting someone else’s work or ideas as your own, with or without their consent, by incorporating it into your work without full acknowledgment”.

It involves:

1. Copying and pasting someone else's text

2. Paraphrasing someone else's work without proper attribution

3. Using someone else's ideas without giving them credit

4. Using someone else's images, videos, or other media without permission

5. Submitting someone else's work, such as a paper or project, as your own

  Is it plagiarism if you copy and paste but cite the source?

Presenting someone’s idea as your own is considered plagiarism if not properly cited. While if you copy and paste but cite the source with proper formatting then it's not plagiarism.

  Is the usage of plagiarism-checking tools Illegal?

Educational institutions highly recommend plagiarism-checking tools to prevent copying of others’ work. Even research work is not considered authoritative without a plagiarism report. So using plagiarism-checking tools is legal and necessary.

  What are the consequences of plagiarism?

Plagiarism involves serious consequences as its a crime.

Its Academic consequences range to:

• Failing the assignment
• Failing the course
• Institute Suspension
• Expulsion from institution

Its Legal consequences cost you:

• Intellectual Damages
• Criminal Charges
• Loss of your employment

Its Personal consequences can affect you with:

• Loss of credibility
• Damage of Reputation
• Damage to professional relationships
• Slowing down your career, especially in Education

We Cover Multiple Platforms

Our plagiarism-checking process supports one of the most popular platforms, WordPress. You can check for plagiarism/duplication of your content directly from your WordPress admin panel. Just download our plagiarism checker plugin and start using it. Check Plagiarism also support educational institutions through our APIs. Universities, colleges, and other educational institutes & Organisations can integrate our APIs to detect plagiarized content on their own end. Our plagiarism tool works accurately on other platforms as well.   

A WordPress plugin is available to check plagiarism directly from the admin panel.

Configure your plagiarism software with our APIs, to check directly from your server.

How it Works

Content is first parsed into sentences and then every sentence is checked for plagiarism. If any duplication is found against that sentence, it will be marked in red and if that text is unique, it will be shown in green.

Comparing your content

All results are displayed in real-time. You can compare your content with any of the matched URLs with just one click. You can also use this plagiarism tool directly on the plagiarism comparison tool page.  

Happy Customers

Our plagiarism checker is loved by 400,000+ customers worldwide. We have positive feedback from customers of all categories. Look at those reviews below.

Our Customers Feedback

We have 70,000+ happy clients including Masters, Students, Teachers and publishers around the world. A Positive previous record is enough for new users to understand that we have the most accurate plagiarism detector.

This free online plagiarism detector is amazing; it checked my content in seconds with very accurate results. According to me it's the best plagiarism detection software.

I am a student and I use this tool regularly to check plagiarism for my papers. Best plagiarism checker for students.

I used their APIs to configure my college website so that students can check plagiarism free for their papers directly from the college website.

Recent Posts

8 Simple Ways To Enhance Your Writing Skills

Content marketing has become one of the most effective strategies for reaching the targeted ...

4 Tools To Ensure Credibility And Trustworthiness In Content

Trust is hard to build and even harder to maintain. Trustworthiness is the most important as...

Understanding Essays And How To Write Them

There’s a lot more to essays than just the stereotypical impression of a dreaded assig...

Rise In Impact Of Content & Why It Matter In Business?

Businesses invest a lot of money in creating a robust marketing strategy to stay relevant in...

(@home_page_title->tool_title)

Please verify the source information and complete any empty fields the best you can..

• Interesting
• Scholarships
• UGC-CARE Journals

14 Best Free Plagiarism Checkers Online – 2024

Best free online plagiarism checkers 2023

Table of contents

Why plagiarism checkers, 5 important questions to ensure before giving your content to a free plagiarism checker, best free plagiarism checkers online overall list, 1. duplichecker.com, 2. plagiarismsoftware.net, 3. plagiarism checkerx, 4. plagscan.com, 5. plagramme.com, 6. smallseotools.com – free plagiarism checker, 7. plagiarisma.net, 8. quetext.com, 9. easybib.com, 10. noplag.com, 11. viper plagiarism checker, 12. copyleaks plagiarism checker, 13. turnitin.com, 14. ithenticate.com.

• How to Choose the Best Plagiarism Checkers

Recent Best Plagiarism Checkers List 2024

Plagiarism is a serious offense that can hurt your academic or professional career. To avoid accusations of plagiarism, it’s important to use a plagiarism checkers to ensure that your work is original.

There are many different plagiarism checkers available, so it can be difficult to choose the right one. In this article, we’ll compare the best plagiarism checkers of 2023 and help you choose the one that’s right for you.

Plagiarism is not happening with intention. most of the time, it appears by mistake in your thesis. Even a single inaccurate citation also considered being plagiarism. To identify plagiarism, there are many free plagiarism checkers available online.

Identifying unoriginal content, reducing similarity, and improving the quality of your thesis with some effective free plagiarism checker tools are very important before submitting your thesis.

In this article, ilovephd provides the 10 free plagiarism checker tools available online for your thesis correction.

Risk Factors of Free Plagiarism Software

When it comes to free plagiarism software, the first thing that comes to your mind is content theft .

There are hundreds of free plagiarism-checking software available online but still, it would be difficult to recommend a free tool to check plagiarism. Let’s see some of the major reasons why researchers refuse to use free plagiarism software.

• How are plagiarism-checking tools earning?
• Does that plagiarism checker store and sell your content?
• How effective that plagiarism checker is?
• Does the end report accurate?

By finding the answer to these questions, you can go further to take a plagiarism report in the free tools. With these important questions, we analyzed more than 50 free tools and shortlisted 10.

Most of the tools given in the list are earned by Google Adsense also ensures the data protection of the unpublished scientific thesis content.

Here are some of the best free plagiarism checkers available online:

• Grammarly: Grammarly offers a free plagiarism checker that scans your text for duplicate content and provides a similarity percentage. It’s user-friendly and integrates well with your writing process.
• Copyscape: Copyscape is a dedicated plagiarism checker that can be used for free to find duplicate content online. It’s especially useful for verifying if your content has been copied by others.
• DupliChecker: DupliChecker provides a free and user-friendly plagiarism checker that scans your content for similarities and generates a plagiarism report.
• SmallSEOTools: SmallSEOTools offers a comprehensive suite of SEO and content-related tools, including a free plagiarism checker. It’s simple to use and provides detailed reports.
• Plagscan: Plagscan offers a limited number of free checks per month. It’s known for its accuracy and is commonly used in academic and research settings.
• Quetext: Quetext offers a free plagiarism checker with a simple interface and the ability to upload documents for scanning. It provides a similarity percentage and highlights potential issues.
• PaperRater: In addition to grammar and spell checking, PaperRater offers a free plagiarism checker that provides an originality score and identifies potential sources of plagiarism.
• Prepostseo: Prepostseo offers a free plagiarism checker that allows you to upload documents or check text directly on their website. It provides a similarity percentage and highlights duplicate content.
• Plagiarisma: Plagiarisma is an online plagiarism checker that supports multiple languages and checks content against a wide range of sources on the web.
• PlagScan (Free Trial): While PlagScan offers a paid service, it also provides a free trial version that allows you to check a limited number of documents for plagiarism

10 Best Plagiarism Checkers Free for Thesis, Research Paper, and Assignment

Duplichecker is a free online plagiarism-checking software developed for proofreading, editing online content, and plagiarism chinking for students and academic writers. this site gives the best accuracy of output with a statistical report by using their tools.

Plagiarism software is one of the famous free plagiarism checkers online . This tool was designed in a way to help academic writers to analysis their text documents. This free plagiarism checker performance accurate and gives all possible details about the document.

This software is designed to detect duplicate content against your given documents/text/thesis which helps students, teachers, universities, colleges, and academic writers to check the originality of their content over plagiarized material.

Plagiarism CheckerX software’s trial version is FREE to download but has some limitations. But still, it performs better in the free version for small chunks of the document.

PlagScan is another leading plagiarism checker tool with strong privacy and legal compliance. PlagScan ensures the uploaded documents will never be shared with unauthorized third parties.

Plagramme plagiarism checking software works deeply to check the citations, online contents, and academic documents. plagramme reports if traces of repeated paraphrasing and copied content are found in your thesis.

Small SEO Tools offers a Best Free Plagiarism detector which gives deep insights of the document with respective copied URLs. This free plagiarism tool helps millions of students and writers to check the plagiarism every day.

Plagiarisma is a free plagiarism checker tool which also available for Android devices. This easy and free plagiarism tool helps you to check plagiarism in an effective way.

  Quetext is a leading plagiarism-detection software, providing services to over 2 million teachers, students, and professionals worldwide. Quetext is to empower writers with a sense of security in their work.

EasyBib Plus plagiarism tool provides all-in-one support to cover all your bases. The premium version of the software covers more futures.

With a single scan, you’ll receive personalized feedback to help identify potentially missing citations and help improve your sentence structure, punctuation, grammar, and more.

Noplag provides a detailed plagiarism report that you can send and share. So don’t let plagiarism issues slow you down anymore, deal with them in the fastest and most effective. it offers various services such as plagiarism checkers for universities, colleges, schools, students, teachers, and academics.

Viper Plagiarism Checker is an online plagiarism detection software that scans documents for potential plagiarism. It is used by students, teachers, and professionals to check for plagiarism in written work. It compares the content of a document to a vast database of online sources, including websites, journals, and other documents, to detect any potential plagiarism.

Copyleaks Plagiarism Checker is an online tool that helps you detect plagiarism in your content. It scans your content for plagiarism against billions of online sources and provides you with an instant report. It also helps you identify the sources of plagiarism and provides you with a detailed report on the plagiarism check. Copyleaks Plagiarism Checker can be used to check academic papers, articles, blog posts, and other types of content.

2 Best Paid Plagiarism Checkers for Thesis, Research paper, and Assignment (Recommended Versions)

Turnitin.com provides a premium version of services to students and institutions with 10 types of plagiarism based on findings worldwide.

Each type has been given an easy-to-remember moniker to help students and instructors better identify and discuss the ramifications of plagiarism in student writing.

iThenticate is the leading provider of professional plagiarism detection and prevention technology used worldwide by scholarly publishers and research institutions to ensure the originality of written work before publication. iThenticate helps editors and authors prevent misconduct by comparing manuscripts against its database of over 60 billion web pages and 155 million content items, including 49 million works from 800 scholarly publisher participants.

iThenticate helps editors and authors prevent misconduct by comparing manuscripts against its database of over 60 billion web pages and 155 million content items, including 49 million works from 800 scholarly publisher participants.

How to Choose the Best Plagiarism Checker s

When choosing a plagiarism checker, there are a few things you’ll want to consider:

• Price:  Plagiarism checkers can range in price from free to hundreds of dollars per year. Choose a plagiarism checker that fits your budget.
• Features:  Some plagiarism checkers offer more features than others. Make sure the plagiarism checker you choose has the features you need, such as a plagiarism report and a grammar checker.
• Ease of use:  Plagiarism checkers should be easy to use. Choose a plagiarism checker that’s easy to understand and navigate.
• Reliability:  Plagiarism checkers should be reliable. Choose a plagiarism checker that has a good reputation and that’s been used by others.

Plagiarism is a serious offense that can have a negative impact on your academic or professional career. To avoid accusations of plagiarism, it’s important to use a plagiarism checker to ensure that your work is original.

Ensuring the originality of your content is essential, and the best plagiarism checkers mentioned above can assist you in avoiding the severe consequences associated with plagiarism.

By considering factors such as content database size, user interface, additional features, and pricing plans, you can select the most suitable plagiarism checker to meet your specific needs. Invest in these reliable tools to enhance your writing integrity and achieve peace of mind.

There are many different plagiarism checkers available, so it’s important to choose the right one for your needs. In this article, we’ve compared the best plagiarism checkers of 2023 and helped you choose the one that’s right for you.

We hope this article helps you to choose the right plagiarism tool to check your thesis, research manuscript, and assignments.

You Might Also Love to Read 10 Types of Plagiarism – Every Academic Writer Should Know

• Turnitin: As a leading plagiarism detection tool, Turnitin excels in its comprehensive content database and its ability to provide links to original sources. It offers a user-friendly interface and is suitable for academic purposes.
• Grammarly: While renowned for its grammar-checking capabilities, Grammarly also offers a powerful plagiarism checker. It is versatile, capable of detecting plagiarism in various types of content, and provides helpful suggestions to improve language usage.
• Copyscape: Copyscape specializes in detecting duplicate content on the internet. With its robust search capabilities, it is an excellent tool for bloggers, website owners, and content creators.
• Plagscan: Plagscan stands out with its advanced plagiarism detection algorithms and integration options. It caters to both academic and professional users, offering accurate and detailed reports.
• DupliChecker: DupliChecker is a user-friendly plagiarism checker that allows you to scan text up to 1,000 words for free. Its simplicity and quick analysis make it suitable for casual users.
• Quetext: Quetext provides a reliable plagiarism detection system with an intuitive interface. Its extensive database and contextual analysis help identify similarities in content effectively.
• Small SEO Tools Plagiarism Checker: Small SEO Tools offers a free plagiarism checker that scans text for duplicate content. It provides a basic but useful service for quick plagiarism checks.
• Unicheck: Unicheck focuses on academic plagiarism detection, offering integration with various learning management systems. Its comprehensive reports and intuitive interface make it an excellent choice for educational institutions.
• Plagramme: Plagramme offers a user-friendly interface and accurate plagiarism detection. It provides detailed reports and is suitable for both academic and professional users.
• Scribbr Plagiarism Checker: Scribbr’s plagiarism checker is specifically designed for students, providing comprehensive reports and guidance to improve academic writing. It integrates seamlessly with other writing tools.
• academic integrity
• avoid plagiarism
• Best Plagiarism Checker
• content originality
• duplichecker
• how to check plagiarism
• how to check plagiarism online free
• ithenticate
• plagiarism checker
• plagiarism checkers
• plagiarism detection
• plagiarism detector
• plagiarism online
• Scribbr Plagiarism Checker
• Small SEO Tools Plagiarism Checker
• top plagiarism checkers

480 UGC-CARE List of Journals – Science – 2024

Ugc-care list of journals – arts and humanities – 2024, choosing a phd supervisor 9 key factors to consider.

[…] 10 Free Plagiarism Checker Tool for Thesis […]

[…] all your Journals, Magazines, Book Chapters, Thesis, and other materials in an organized […]

The info talked about within the write-up are a number of the best offered.

This list is very useful and can across some of the new tools with the help of this list. Thank !

Nice!! This article offers tips on how to write a research paper without plagiarising, which is crucial.

Great!! This is an effective tool for checking for plagiarism. The collection of resources is excellent for essay writers.

Hi team! How do I get a plagiarism check/type that that check an unlimited words in a manuscript for free of charge?

Thancks great dr

LEAVE A REPLY Cancel reply

Notify me of follow-up comments by email.

Notify me of new posts by email.

Email Subscription

iLovePhD is a research education website to know updated research-related information. It helps researchers to find top journals for publishing research articles and get an easy manual for research tools. The main aim of this website is to help Ph.D. scholars who are working in various domains to get more valuable ideas to carry out their research. Learn the current groundbreaking research activities around the world, love the process of getting a Ph.D.

WhatsApp Channel

Join iLovePhD WhatsApp Channel Now!

Contact us: [email protected]

Copyright © 2019-2024 - iLovePhD

• Artificial intelligence

• Open access
• Published: 16 October 2023

Using AI-based detectors to control AI-assisted plagiarism in ESL writing: “The Terminator Versus the Machines”

• Karim Ibrahim   ORCID: orcid.org/0000-0003-0037-0872 1  

Language Testing in Asia volume  13 , Article number:  46 ( 2023 ) Cite this article

3894 Accesses

4 Citations

9 Altmetric

Metrics details

A Correction to this article was published on 31 October 2023

This article has been updated

The release of ChatGPT marked the beginning of a new era of AI-assisted plagiarism that disrupts traditional assessment practices in ESL composition. In the face of this challenge, educators are left with little guidance in controlling AI-assisted plagiarism, especially when conventional methods fail to detect AI-generated texts. One approach to managing AI-assisted plagiarism is using fine-tuned AI classifiers, such as RoBERTa, to identify machine-generated texts; however, the reliability of this approach is yet to be established. To address the challenge of AI-assisted plagiarism in ESL contexts, the present cross-disciplinary descriptive study examined the potential of two RoBERTa-based classifiers to control AI-assisted plagiarism on a dataset of 240 human-written and ChatGPT-generated essays. Data analysis revealed that both platforms could identify AI-generated texts, but their detection accuracy was inconsistent across the dataset.

Introduction

In November 2022, the world was taken aback by the public release of OpenAI’s ChatGPT, an interactive artificial intelligence chatbot powered by GPT-3.5 (generative pre-trained transformative model). GPT-3.5 is a natural language processing (NLP) model (OpenAI, n.d . - a) that can respond to users’ requests as a conversation agent by performing a variety of NLP tasks, including translating texts, generating texts, summarizing content, gathering information, telling stories, composing music, or developing computer code (Gao et al., 2023 ; Khail & Er, 2023 ; Cotton et al., 2023 ; Lund & Wang, 2023 ; Yeadon et al., 2022 ). Along with its ability to perform a variety of cognitive tasks (Susnjak, 2022 ), ChatGPT’s impressive ability to generate coherent, intelligible, humanlike language (Cotton et al., 2023 ) has attracted 100 million users in 2 months, making it the fastest-growing internet app ever created (Hu, 2023 ) with an average of 1.8 billion visits per month (Carr, 2023 ). OpenAI’s GPT-3.5 is not the first large language model (LLM), but it represents a quantum leap in language modeling performance that supersedes competing natural language models currently available (Brown et al., 2020 ; Chen et al., 2023 ; Ouyang et al., 2022 ). This leap has produced a model with an unprecedented ability to emulate human cognitive abilities concerning NLP, complex problem-solving, and critical thinking (Solaiman et al., 2019 ; Lund & Wang, 2023 ; Radford, 2018 ; Radford, 2019 ). Thus, the release of ChatGPT marked a revolutionary transformation in the NLP capabilities of artificial intelligence and the dawn of a new era of automation of what until recently were believed to be uniquely human capabilities.

Despite the tremendous potential implications of ChatGPT technology for different fields, the evolutionary development of ChatGPT into a brilliant and expansive technology capable of simulating human intelligence and able to learn and encompass new expertise seamlessly (e.g., Bommarito II & Katz, 2022 ) has sent shockwaves in every field, shifting how users view AI-technologies (Haque et al., 2022 ). Ironically, similar to Schwarzenegger’s Terminator movie franchise, where humans panicked upon realizing that AI can give machines the power to revolt against the human rule, academics and thinkers in every field are wondering about the potential misuses of the technology and how they could affect every facet of modern life from swaying public opinion in politics to transforming the face of the job market in economics and everything in between (e.g., Zeller et al., 2019 ). One of the seminal concerns about ChatGPT (and generative language models in general) is the potential misuse of its generative abilities for plagiarizing in higher education (Haque et al., 2022 ). Specifically, with the ability of ChatGPT to generate coherent texts (Cotton et al., 2023 ) and respond to critical questions (Susnjak, 2022 ), there is a growing fear that it could be widely utilized by college students to complete essay assignments (Francke & Benett, 2019 ; King & ChatGPT, 2023 ; Yeadon et al., 2022 ). Thus, the disruptive and sudden rise of ChatGPT as a powerful generative technology marks a new age of AI-assisted plagiarism that constitutes an existential threat to higher education as it brings into question the reliability of its assessment practices and could devaluate the college degree (Cotton et al., 2023 ). The threat of AI-assisted plagiarism is more eminent in ESL composition contexts where educators are challenged by the wide prevalence of plagiarism (Pecorari & Petric, 2014 ) either because ESL students do not fully grasp the Western concept of plagiarism (Ali et al., 2012 ) or because they draw on it as a resource to compensate for their limited L2 proficiency (Waltzer & Dahl, 2023 ). And, in the face of this existential threat, ESL educators have very little guidance or reliable resources in detecting AI-generated texts (e.g., Mitchell, 2022 ), especially since human raters have difficulty distinguishing between AI-generated and human-written texts (Brown et al., 2020 ; Hovy, 2016 ; Ippolito et al, 2019 ; Solaiman et al., 2019 ) and traditional plagiarism detection software fail to detect AI-generated texts (Khalil & Er, 2023 ).

With the rising fear of AI-generated texts’ proliferation, a growing body of research in NLP has explored different approaches to detecting AI-generated texts (e.g., Bakhtin et al., 2019 ), including watermarking AI-generated texts (e.g., Kirchenbauer et al., 2023 ), detecting traces of language modeling in generated texts (e.g., Tay et al., 2020 ), and training AI models to discriminate between AI-generated and human-written texts (e.g., Fangi et al., 2021 ). One of the promising detection approaches that can help ESL educators control AI-assisted plagiarism is training an AI-based classifier (i.e., a program that classifies texts based on specific criteria) to distinguish between human-written and AI-generated texts (Jawahar et al., 2020 ). This way, AI-based classifiers can use the power of AI to identify AI-generated text and aid educators in controlling AI-assisted plagiarism. Early research in this field suggests that fine-tuned classifiers (Solaiman et al., 2019 ; Tay et al., 2020 ; Mitchell et al., 2023 ), which are complex classifiers based on generative models and trained further on text detection, can achieve higher accuracy in detecting AI-generated texts compared to other classifiers (e.g., Fangi et al., 2021 ). Specifically, fine-tuned classifiers based on RoBERTA model (Liu et al., 2019 ), a fine-tuned AI detector trained on the output of existing large language models (e.g., GPT-2), has demonstrated higher detection accuracy compared to other classifiers (Solaiman et al., 2019 ; Jawahar et al., 2020 ; Fagni et al., 2021 ). However, these findings are preliminary, and further research on the model and parameters of its use is needed to develop reliable AI classifiers. Alongside NLP research, several plagiarism-detection companies and language modeling companies responded to the growing fears of AI-assisted plagiarism by developing online AI text detection platforms; however, most of these commercial platforms are experimental, and their providers suggest that their results are not always accurate. For instance, Turnitin Feedback Studio warns against using the results of its AI detector in making decisions about academic integrity. Turnitin reported that in-house experiments on their AI detector found that essays flagged as potentially AI-generated with a less than 40% probability are likely to be false positives (Turnitin, 2023 ). In addition, most commercially available AI-detection platforms are powered by simple detection models (i.e., simple classifiers), which NLP research suggests are of limited reliability (e.g., Solaiman et al., 2019 ). The questionable reliability of commercial detectors limits their applicability in controlling plagiarism, as decisions about academic integrity need to be based on solid grounds. The fact that available research on AI-text classifiers is still maturing and commercial detectors are still experimental leaves L2 educators with almost no viable resource to face the challenge of AI-assisted plagiarism. This predicament underlines the pressing need for cross-disciplinary research in L2 assessment that draws on NLP research on AI-text generation and detection to evaluate classification approaches and commercial detection platforms to identify viable and reliable AI-detection approaches that can help L2 educators control AI-assisted plagiarism.

To address the challenges of AI-assisted plagiarism in ESL writing contexts and explore this uncharted research territory, the present cross-disciplinary descriptive study examined the potential of AI-based classifiers to detect AI-assisted plagiarism in ESL composition classes. Specifically, the researcher evaluated the performance of RoBERTa-based AI text detection platforms: GPT-2 Output Detector Demo and Crossplag Detector . These platforms were selected because they are (a) based on a robust detection model supported by empirical research (i.e., RoBERTa) and (b) are publicly available to educators for free. Both detectors were trained on GPT-2 data (OpenAI, n.d. -b). The study will use each detector separately to evaluate a data set of AI-generated and human-written texts and then compare the performance of both detectors. It is hoped that this line of research can shed light on (a) the effectiveness of publicly available AI-based detectors in controlling AI-assisted plagiarism, (b) the effectiveness of RoBERTa model in AI text classification (in realistic contexts with longer, more sophisticated, and more diverse texts), and (c) the reliability of detectors trained on earlier versions of GPT in the face of more recent and larger versions of the model (given that both detectors were trained on GPT-2 and are used to detect the outputs of GPT-3.5). For discussion, AI- , machine-generated , and AI-generated texts will be used interchangeably to refer to texts generated by an AI language model. This paper will start with an overview of early research on the implications of ChatGPT for plagiarism and a survey of different approaches to detecting machine-generated texts. After that, the methodology and research questions of the study will be presented. Next, the study’s findings will be presented, analyzed, and discussed in light of current research. Finally, the study will conclude with implications, limitations, and directions for future research.

Literature review

ChatGPT is an interactive artificial intelligence chatbot (i.e., an internet-based computer program that can interact verbally with users) released by OpenAI on November 30, 2022 (OpenAI, n.d. - a). ChatGPT has gained massive popularity among internet users worldwide due to its unique ability to generate intelligible and coherent natural language texts that are difficult to distinguish from human-written ones. The chatbot can perform different language processing tasks, including answering complex questions, generating articles, poems, stories, or computer code; and reading, summarizing, paraphrasing, editing, or translating texts. The chatbot is powered by the Generative Pre-trained Transformer model (GPT-3.5), a 175B parameter auto-regressive natural language processing model (Radford et al., 2018 ). Footnote 1 As a generative language model (Pavlik, 2023 ), GPT is a machine-learning neural network trained to analyze and interpret natural language texts and model the linguistic system of a language (MacNeil et al., 2022 ). After training, generative models can transfer their understanding of natural language systems to new language processing contexts and generate coherent natural language texts (Pan et al., 2010 ). A generative model’s capacity is measured by the number of parameters (i.e., modeling criteria acquired in training) used to model a language and generate texts. GPT is not the only generative language model (other models include Megatron-Turing Natural Language Generation by Nvidia , BARD Model by Google , and Chinchilla and Gopher AI by DeepMind ). Still, the reason for GPT’s outstanding performance and immense popularity lies in how the model was developed through iterative cycles of innovative machine-learning training.

The most common deep-learning approach used to train a language model is supervised training, which involves training a model on large quantities of labeled data (i.e., tagged texts) to perform task-specific linguistic functions (Radford, 2019). Supervised training involves training a machine learning system on a dataset of examples exhibiting correct behavior on a specific linguistic task so that the system imitates that behavior on similar tasks. However, the use of single-task training (i.e., examples exhibiting correct behavior on one task) on single classes of datasets (i.e., types of texts) has limited the generalizability of these systems (i.e., they have problems generalizing correct behaviors to different kinds of texts or domains), which impacts the performance of a language model (Radford, 2019 ). The alternative to supervised training is unsupervised training, which entails training a model on a vast dataset of unlabeled data (i.e., text samples), allowing the model to detect the underlying structures and patterns of a language system from the data (Lee et al., 2018 ); however, unsupervised training required vast quantities of data, which makes this type of training expensive and time-consuming (Radford, 2018 ). To overcome the challenges of unsupervised training and the limitations of supervised training, OpenAI developed an innovative semi-supervised approach that combines unsupervised pretraining (i.e., initial training of a language model) and supervised fine-tuning (i.e., more specific further training of a pre-trained model) that they used to train their GPT model (OpenAI, n.d. -a). OpenAI used Common Crawl to train the model on a dataset of a trillion words of internet texts (175 billion parameters) in supervised training (Brown, 2020 ) and then fine-tuned the model using reinforcement learning from human feedback approaches (Christiano et al., 2017 ) to improve output alignment and consistency with users’ intent, along with improving task-agnostic performance (i.e., modeling a language across different functions and task) and transfer learning capabilities (Ouyang et al., 2022 ). As a result of this innovative hybrid training approach, ChatGPT can generate humanlike texts that are difficult to distinguish from human-written ones. This powerful ability brings into question the potential misuse of the technology to facilitate plagiarism.

• AI-assisted plagiarism

The impressive ability of ChatGPT to engage in critical thinking and generate coherent, fluent texts that are difficult to distinguish from human-written ones has raised serious concerns about potential misuses of the technology to plagiarize on college assessments as students can submit machine-generated texts as their work (Francke & Bennet, 2019 ; Haque et al., 2022 ; Yeadon et al., 2022 ; Cotton et al., 2023 ; Susnjak, 2022 ; King & ChatGPT, 2023 ; Khalil & Er, 2023 ; Gao et al., 2023 ). As Yeldon et al. ( 2022 ) put it:

“We may be at the beginning of an AI revolution. In order to facilitate authentic assessment it is vital that we are aware of the capabilities of this technology and its ramifications on the way that credited work is assessed. In the present case, it is hard to avoid the conclusion that non-invigilated assessments based on short-form essay questions are already no longer fit for purpose; they are simply too vulnerable to current AI text-completion technologies, which can produce creditable content cheaply and quickly” (p.11-12).

Not only can AI-assisted plagiarism undermine higher education and devaluate college degrees by misrepresenting students’ educational performance on course assessments (Cotton et al., 2023 ), but they are also more challenging to identify using traditional plagiarism detection approaches adopted by higher education institutions since they constitute original texts that were not reproduced and do not get flagged on traditional plagiarism detection systems (Khalil & Er, 2023 ). These severe concerns have led to some preliminary investigations into the potential of ChatGPT to facilitate AI-assisted plagiarism.

One of the earlier attempts to probe into the potential misuses of ChatGPT to violate academic integrity was Francke and Bennett’s ( 2019 ) case study investigating the potential impacts of GPT-2 model (OpenAI, n.d. -b) on the proliferation of plagiarism in higher education. Using focus-group interviews, the researchers gathered data from two groups of academics on the quality of ChatGPT-generated texts and their potential to spread plagiarism in higher education. Data analysis revealed that AI can generate intelligent texts that simulate human writing and increase the proliferation of plagiarism in higher education. Similarly, but with a broader focus on internet users’ reaction to generative AI, Haque et al. ( 2022 ) conducted a sentiment analysis of 1732 Tweets from early adopters of ChatGPT to explore the public response to the new technology. Using a mixed-methods design and topic modeling of discussions, data analysis revealed that early adopters of the technology perceived ChatGPT to be a disruptive technology that is likely to transform traditional education (both in positive and negative ways) and expressed serious concerns that students can misuse it to plagiarize on homework assignments. Also, a few studies have examined ChatGPT’s perspectives on the implications of generative AI for academic integrity. For example, Cotton et al. ( 2023 ) explored ChatGPT’s views on the opportunities and challenges it poses to higher education. They reported that one of the critical challenges the system acknowledged was the possibility that students could misuse ChatGPT’s ability to generate coherent texts to commit plagiarism. Similarly, King and ChatGPT ( 2023 ) inquired about ChatGPT’s opinion on college students’ potential misuse of ChatGPT to cheat on essay assignments and reported that the platform supported the possibility that students can feed their assignments to ChatGPT as prompts and present generated responses as their original work.

A few studies have also explored the potential of generative AI to facilitate plagiarism by investigating its ability to engage in critical thinking and generate coherent academic essays. For instance, Susnjak ( 2022 ) explored the ability of ChatGPT to engage in critical thinking by evaluating its output in response to critical thinking questions. Using the universal intellectual standards (Paul, 2005 ) to measure logical reasoning and critical thinking, the researcher analyzed the model’s responses for clarity, accuracy, precision, depth, breadth, logic, persuasiveness, and originality. Data analysis revealed that ChatGPT can understand the context of a prompt, engage in critical thinking, and generate logical and coherent responses, demonstrating depth and breadth of knowledge. The researchers concluded that ChatGPT can reason critically and express its ideas in logical, coherent prose that is indistinguishable from human-written texts and, thus, poses a severe concern for academic integrity.

Similarly, but with an explicit focus on essay composition skills, Yeadon et al. ( 2022 ) investigated the threat of AI-generated essays to academic integrity by examining the quality of academic papers generated by ChatGPT. Using an essay-question prompt from a Physics course, the researchers gathered ten AI-generated scripts and had them marked independently by five human raters. The samples achieved an average score of 71%, close to the average student score in the course. The researcher inferred from the results that students in the lowest third of the cohort would have a solid motive to use ChatGPT to complete their assignments. In another study, Bommarito and Katz ( 2022 ) explored the ability of ChatGPT to complete tasks that require depth of knowledge and complex semantic understanding by having the system take the MCQ section of the BAR exam. The study reported that ChatGPT achieved 50.3% on the practice exam and concluded that ChatGPT exhibited an advanced ability to understand complex questions and gain depth of knowledge in domain-specific tasks.

Even though available research on AI-assisted plagiarism is scarce in quantity, broad in scope, and exploratory in nature, it indicates that ChatGPT (and generative AI) could pose a serious threat to academic integrity and calls into question available means of detecting AI-generated texts as fundamental resources to controlling AI-assisted plagiarism.

AI-generated text detection

In response to the growing concerns about potential misuses of generative AI, such as the mass-generation of web texts (e.g., tweets) to spread misinformation or manipulate public opinion (e.g., Zeller et al., 2019 ), NLP researchers trained text classification algorithms to classify texts as either human-written or machine-generated as a means to detect machine-generated texts (Solaiman et al., 2019 ). Classifiers are trained to identify the characteristics of a machine-generated text based on their semantic understanding of a text (Ippolito et al., 2019 ). Machine-generated text detection classifiers fall into three categories based on their underlying detection mechanism and training approach: (1) simple classifiers, (2) zero-shot classifiers, and (3) fine-tuned classifiers (Jawahar et al., 2020 ).

Simple classifiers

Simple classifiers are basic machine-learning algorithms trained from scratch to discriminate between human-written and machine-generated texts (Solaiman et al., 2019 ). A basic form of this classifier is the “bag-of-words,” which is typically a logistic regression model trained on large sets of machine-generated and human-written texts to identify subtle stylistic or linguistic differences between the two categories (Jawahar et al., 2020 ). Solaiman et al. ( 2019 ) experimented with training a simple classifier using logistic regression to discriminate between GPT-2 output and internet articles across different model sizes and sampling methods. The researchers found that detection accuracy varied depending on the generative model’s size, as texts generated by larger models were more challenging to detect. Another form of simple classifiers is machine-configuration detectors, which distinguish machine-generated texts by identifying distinct traces of the modeling choices used to generate a text (e.g., model size, decoding method, or model architecture) and use these traces to determine the origin of a text (Jawahar et al., 2020 ). For instance, Tay et al. ( 2020 ) empirically investigated the potential use of textual artifacts that appear in machine-generated texts due to modeling and sampling choices to identify their generative language model. Using texts generated by GROVER (Zeller et al., 2019 ) language model in different modeling conditions (e.g., model size) and the CNN/Dailymail news corpus (as human-written texts), the researchers trained and tested a classifier model to distinguish between texts based on modeling choices’ artifacts. The experiments revealed that modeling choices left artifacts in the generated texts that could be used to predict modeling choices based on the generated texts alone and could be the basis of a new approach to machine configuration detection. These studies suggest that large language models challenge simple classifiers trained from scratch. Still, they can be used to identify traces of a language model in a generated text, which can contribute to future innovations in AI-text detection.

Zero-shot classifiers

Zero-shot classifiers employ a pre-trained generative model (e.g., GPT-2) to identify texts generated by itself or comparable models without additional supervised training (Jawahar et al., 2020 ). An example of a zero-shot classifier is Gehrmann et al. ( 2019 )’s Giant Language Model Test Room (GLTR), which utilizes BERT (Devlin et al., 2018 ) and GPT-2 (Radford et al., 2019 ) models to discriminate between machine-generated and human-written texts based on the probability of word distribution in these models (i.e., the likelihood that the distribution of words in a text was generated by a given model). The researchers tested GLTR empirically with machine-generated and human-written articles from different sources. The analysis revealed that GLTR offered frequency and ranking information that improved human raters’ accuracy in detecting machine-generated texts. Similarly, Solaiman et al. ( 2019 ) used a zero-shot detector based on GPT-2 model to assess text origin based on total log probability (i.e., GPT-2 estimation of the likelihood that word distribution in a text is similar to its own generated text). However, they found that it performed poorly compared to a simple classifier. Similarly, Mitchell et al. ( 2023 ) developed a zero-shot classifier based on GPT-3, DetectGPT, that detects GPT-generated texts based on their log probability. The researchers conducted several experiments comparing the performance of DetectGPT to previous zero-shot and two fine-tuned classifiers. The experiments revealed that DetectGPT outperformed previous zero-shot classifiers, but underperformed fine-tuned classifiers on general domain topics. These studies indicate that zero-shot classifiers perform poorly in AI text detection but can offer data that aid human-rating of AI-texts.

Fine-tuned classifiers

These classifiers are pre-trained generative models that are fine-tuned for text classification with supervised training on detection examples. An example of fine-tuned classifiers is Liu et al. ( 2019 )’s fine-tuning of BERT model (Delvin et al., 2019), RoBERTa (Robustly optimized BERT approach), which uses a modified training approach involving longer training cycles, bigger training batches, and longer data sequences. The researchers trained RoBERTa with over 160 GB of text and found that RoBERTa’s optimized training methods, especially data size and variety, resulted in performance improvement compared to BERT. Similarly, Zeller et al. (2019) developed a linear classifier with the GROVER model to detect its own generated fake news articles. The researchers compared GROVER fine-tuned classifier to several fine-tuned classifiers: BERT, GPT-2, and FastText. The results revealed that the fine-tuned GROVER classifier outperformed other fine-tuned classifiers in detecting its own generated texts. Similarly, Solaiman et al. ( 2019 ) fine-tuned RoBERTa classifier to detect GPT-2-generated texts and found that it could detect GPT-2-generated texts with a higher accuracy than a GPT-2-based detector. Similarly, Jawahar et al. ( 2020 ) experimented with using RoBERTa to distinguish between GPT-2-generated and human-written Amazon reviews. They found that RoBERTa detector needs training with several thousands of examples to achieve high accuracy in detection. In another study, Fagni et al. ( 2021 ) used a deepfake and human-written tweets database to compare the performance of simple, zero-shot, and fine-tuned classifiers. The researchers classified 25,572 tweets using a simple Bag-of-word classifier, BERT model (as a zero-shot classifier), and several fine-tuned classifiers: XLNet. RoBERTa, BERT, and DistilBERT. The experiments revealed that fine-tuned classifiers outperformed zero-shot and simple classifiers and that RoBERTa achieved the highest accuracy, outperforming other fine-tuned classifiers by a wide margin. These studies revealed that fine-tuned classifiers, especially RoBERTa model, can outperform other classifiers in AI-text detection.

This brief survey of the literature on AI-based classifiers suggests that fine-tuned classifiers, especially RoBERTa-based classifiers, constitute the most promising resources for detecting machine-generated texts; however, research has barely scratched the surface of this novice area, and further research is needed to gain a deeper understanding of the strengths and limitations of RoBERTa-based classifiers.

In addition, the only access that L2 educators have to AI classifiers is through commercial platforms claiming the ability to detect AI-generated content, including GPTZero , Originality.AI , ZeroGPT , AI Writing Indicator (by Turnitin), and Writefull. However, most of these platforms are still in their beta version (i.e., drawing on user interaction as training to improve their performance); thus, they cannot provide a reliable measure of text originality that constitutes valid grounds for making assessment decisions in higher education. For instance, OpenAI has developed an AI Text Classifier based on a fine-tuned GPT model to predict the likelihood that a text is machine-generated, but they indicated that “the classifier is not always accurate; it can mislabel both AI-generated and human-written text” (OpenAI, n.d. -b). Moreover, despite the growing research on text classifiers, most of the available commercial AI-detection platforms rely on training simple classifiers in-house and do not refer to any theoretical or empirical foundations for their platforms, which brings the validity of their results into question.

One of the few publicly available detection platforms that adopted an empirically supported underlying classification mechanism is GPT-2 Output Detector Demo . This fine-tuned classifier was developed by training RoBERTa model (Liu et al., 2019 ) with the output of the 1.5 billion GPT-2 model (OpenAI, n.d. -b). This classifier generates a score representing the probability that the examined text is human-written. OpenAI claims that in their in-house testing of the classifier, it achieved a 95% accuracy in machine-generated text detection. However, they warn against using its results to make “allegations of academic misconduct” (Hugging Face). Another available platform based on a fine-tuned classifier is Crossplag’s AI Content Detector . This classifier is also based on RoBERTa model and was fine-tuned using OpenAI’s GPT-2 1.5 billion parameters dataset (Crossplag). Crossplag’s classifier generates a score indicating the probability that the examined text is machine-generated. Like OpenAI, Crossplag suggests that their classifier can accurately detect machine-generated texts. However, they also suggest that their platform is still in beta and unavailable for institutions (Crossplag). So even though these detection platforms have research foundations (i.e., RoBERTa model), they still need further testing to ensure the reliability of their results and the validity of academic integrity decisions that can be made based on them. To date and the researcher’s knowledge, only one study has examined the reliability of an AI-text detection platform. Gao et al. ( 2023 ) examined the reliability of GPT-2 Output Detector Demo by comparing its originality scores for 50 human-written and 50 ChatGPT-generated abstracts. The researchers suggested that the detector accurately discriminated between original and generated abstracts. However, the wide range of variability in the results and the small sample size warrant further investigation. To extend this critical line of research, improve our understanding of methods for controlling AI-assisted plagiarism, and guide L2 educators in using AI-detection platforms, the present study will investigate the performance of AI-based detection platforms. And, given the empirical support for RoBERTa model and in line with Gao et al. ( 2023 ) suggestion to explore different platforms, the present study will test and compare the performance of GPT-2 Output Detector Demo and Crossplag AI Content Detector.

Methodology

Generally speaking, descriptive research is well-suited for investigating L2 teaching and assessment since controlled experimental research is challenging to achieve in classroom settings (Nassaji, 2015 ). Given that the present study is exploring a new area where previous research is lacking and understanding is limited, descriptive research approaches would be most appropriate (Fireman Kramer, 1985 ). Within a descriptive research paradigm, the researcher utilized a comparative research design that involves comparing the dependent variables in several groups without manipulating the independent variable (Baker, 2017 ). Specifically, the study examined the performance of two AI-detection platforms, GPT-2 Output Detector, and Crossplag Detector, by analyzing and comparing their originality scores for 120 human-written and 120 machine-generated essays. In this design, the text type (i.e., human-written or machine-generated) constitutes the independent variable. The originality scores, defined as a percentage reflecting the probability that a text is human-written, comprise the dependent variable. The study investigated the following research questions:

How effective is GPT-2 Output Detector Demo in detecting machine-generated essays?

How effective is Crossplag AI Content Detector in identifying machine-generated essays?

What is the difference in classification effectiveness between GPT-2 Output Detector Demo and Crossplag Detector?

What does the comparison of two RoBERTa-based detectors suggest about the robustness of AI-detection of AI-generated texts?

Data collection

For the human-written dataset, a convenience sample of 120 student essays was gathered from a research-argument project in an advanced first-year composition course offered at a major American university in Kuwait in Fall 2021, Spring 2022, and Summer 2022. The researcher gathered samples from previous semesters before ChatGPT was publicly available (in November 2022) to ensure that the essays collected were human-written and that the chances of sample contamination with AI-generated essays were minimal. As for the machine-generated dataset, the researcher asked ChatGPT to generate 120 essays using the January 2023 version (GPT-3.5). The researcher used the research argument project description as a prompt for ChatGPT to ensure that the machine-generated dataset is consistent with the human-written dataset in format and development specifications (e.g., length, number of paragraphs, number of references) and minimize the chances of stylistic inconsistencies that can trigger text detection. To make the machine-generated dataset more comparable to the human-written one, the researcher specified the topics for the machine-generated essays in the prompt and matched them to those of the human-written dataset. Generated essays were transformed into plain texts, excluding the titles and references to minimize stylistic inconsistencies that could flag machine-generated texts. Both datasets were fed into GPT-2 Output Demo Detector and Crossplag Detector to generate originality scores for each dataset.

Data analysis

To measure the effectiveness of each platform in detecting machine-generated texts, the originality scores for the human-written and machine-generated datasets were compared and analyzed for each detector separately using descriptive and inferential statistical methods. Descriptive data analysis from both platforms revealed that the data did not meet the normal distribution condition required for parametric inferential statistical analysis of variance (ANOVA or T -tests). So, non-parametric tests were used to analyze the differences between the two datasets based on grade ranking of scores (Lowie & Seton, 2013 ). Data were analyzed using IBM SPSS version 28. To compare the effects of text type on text originality scores, Mann–Whitney U tests were performed on the originality scores of both datasets for each platform. GPT-2 Output Detector Demo generates an originality score consistent with the study’s definition of the construct of “originality.” So, GPT-2 Outputs were used without modification. Crossplag, on the other hand, generates a score representing the probability that the examined essay was machine-generated (a 0% score means that the platform is confident that an essay is human-written). So, Crossplag originality scores were modified according to the formula originality score  =  100-Crossplag scores to generate scores consistent with the study’s operationalization of the construct of originality and comparable to GPT-2 output Detector Demo Demo scores. After the scores for both datasets were compared for each detector to assess its ability to identify machine-generated texts, a confusion matrix (Fawcett, 2006 ) was developed to get a more accurate estimate of each detector’s accuracy. A confusion matrix is a predictive analysis typical in machine learning research that uses probability data to predict the precision and accuracy of a model (Deng et al., 2016 ). Confusion matrixes can offer a reasonable accuracy estimate as they determine the percentage of false positives and false negatives an algorithm makes. Finally, the originality scores for both data sets were compared between the two detectors to compare the performance of both detectors and shed some light on the effectiveness of RoBERTa-based detectors.

GPT Output Detector Demo

To get a sense of originality scores’ distribution for human-written and machine-generated datasets, the researcher conducted a descriptive statistical analysis of each set of scores before comparing the score distribution for the two sets graphically (see Figs. 1 and 2 ). In the human-written data set (Fig.  1 ), descriptive statistics revealed that data had a mean of 91.4% and a standard deviation of 24.06, but the frequency distribution demonstrated that the data distribution violated the normal distribution assumption (skewness =  − 2.98; kurtosis = 7.77). The data distribution varied widely, with a range of 99.95% (minimum score = 0.03, maximum score = 99.98). The data was uni-modal with a median score of 99.98% and an interquartile range of 0.41% (25th percentile = 99.56%, 75th percentile = 99.98%). The researcher also used a Confusion Matrix analysis to assess the accuracy of the originality scores for the human-written essays by calculating the percentage of true and false positives that the detector generated. Using a 95% confidence interval for originality score accuracy (with a 5% margin of error), the researcher used 95% or above as an accurate estimate of human-written texts’ originality to determine the percentage of false negatives (i.e., human-written essays that are mistakenly assumed to have low originality). Out of 120 essays in the human-written dataset, only 16 essays received an originality score below 95%, representing 13.3% of false negatives versus 86.7% of the data that were true positives.

Histogram representing the frequency distribution of originality scores for human-written texts in GPT-2 Output Detector Demo

Histogram representing the frequency distribution of originality scores for machine-generated texts in GPT-2 Output Detector Demo

Descriptive analysis of the machine-generated dataset (Fig.  2 ) revealed that data had a mean of 3.07% and a standard deviation of 14.99%, but the frequency distribution demonstrated that the data distribution violated the normal distribution assumption (skewness = 5.03; kurtosis = 26.57). The data distribution varied widely, with a range of 99.93% (minimum score = 0.02, maximum score = 99.93). The data was uni-modal with a median score of 0.02% and an interquartile range of 0.01% (25th percentile = 0.02%, 75th percentile = 0.03%). The researcher also used a Confusion Matrix analysis to assess the accuracy of the originality scores for the machine-generated dataset by calculating the percentage of true and false negatives that the detector generated. Using a 95% confidence interval for originality score accuracy (with a 5% margin of error), the researcher used 5% or less as an accurate measure of machine-generated texts’ originality to determine the percentage of false positives (i.e., machine-generated texts assumed to have a degree of originality). Out of 120 essays in the machine-generated dataset, only ten essays received an originality score above 5%, representing 8.3% false positives versus 91.7% of the data that were true negatives.

To compare the scores of both data sets and explore the ability of the detector to discriminate between human-written and machine-generated essays, the researcher used descriptive statistical analysis to visualize the difference between the originality scores for both data sets (see Figs. 3 and 4 ). Firstly, the researcher charted a simple bar graph of mean originality scores for each data set to compare the two data sets and visualize the ability of the platform to classify essays (Fig.  3 ). The bar graph revealed that the scores were polarized between the two datasets.

Simple bar graph comparing the mean originality scores for human-written and machine-generated texts in GPT-2 Output Detector Demo

Histogram comparing the mean originality scores for human-written and machine-generated texts in GPT-2 Output Detector Demo

The researcher also used SPSS to generate a histogram of originality scores classified by dataset (Fig.  4 ) to further explore the scores’ distribution. The histogram demonstrated that the distribution of originality scores was mostly polarized as most human-written essays received an originality score in the 99th percentile, and most machine-generated essays received an originality score within the 1st percentile. However, the scores of some of the essays from both datasets varied widely and overlapped with the other dataset.

To measure the significance of the difference between the two datasets’ mean scores, the researcher conducted a Mann–Whitney U test (Fig.  5 ), a non-parametric alternative to Independent-samples t -tests. The Mann–Whitney U test revealed a significant difference between the human-written and machine-generated essays’ originality scores ( U  = 163, P  = 0.001), with a mean rank of 61.86 for the machine-generated essay dataset and 179.14 for the human-written dataset.

Mann–Whitney U test comparing the mean originality scores for human-written and machine-generated texts in GPT-2 Output Detector Demo

Crossplag Detector

Following the same data analysis used for GPT-Output Detector Demo, the researcher conducted a descriptive statistical analysis of the originality scores of both datasets in Crossplag Detector separately before comparing them. In the human-written dataset (Fig.  6 ), descriptive statistical analysis revealed that the data had a mean originality score of 92.06% and a standard deviation of 21.43; however, the data distribution revealed a violation of the normal distribution assumption (skewness =  − 3.57, kurtosis = 12.19). The data distribution varied considerably, with a range of 100% (min. = 0.001, max = 100). The data were unimodal with a median score of 99% and an interquartile range of 0.00002% (25th percentile = 99.00001%, 75th percentile = 99.00003%). Using a 95% confidence interval for originality score accuracy, the researcher used 95% or above to measure human-written texts’ originality and estimate the percentage of false negatives. Out of 120 essays, only 18 essays received an originality score below 95%, representing 15% false negatives versus 85% of the data that were true positives.

Histogram representing the frequency distribution of originality scores for human-written texts in Crossplag Detector

Descriptive analysis of the machine-generated dataset originality scores (Fig.  7 ) revealed that data had a mean of 3.52% and a standard deviation of 15.40, but the frequency distribution demonstrated that the data violated the normal distribution assumption (skewness = 4.95; kurtosis = 24.78). The data distribution varied widely with a range of 99% (minimum score = 0.001, maximum score = 99). The data was unimodal with a median score of 0.00001% and an interquartile range of 0.00002% (25th percentile = 0.00001%, 75th percentile = 0.00003%). Using a 95% confidence interval for originality score accuracy, the researcher used 5% or less as an average measure of machine-generated texts’ originality to estimate the percentage of false positives (i.e., machine-generated texts assumed to have a degree of originality). Out of 120 essays, only nine essays received an originality score above 5%, representing 7.5% false positives versus 92.5% of the data that were true negatives.

Histogram representing the frequency distribution of originality scores for machine-generated texts in Crossplag Detector

To compare the scores of both data sets and explore the ability of Crossplag Detector to discriminate between human-written and machine-generated essays, the researcher used descriptive statistical analysis to visualize the difference between the originality scores for both data sets. Firstly, the researcher charted a simple bar graph of mean originality scores for each data set to compare the two data sets and visualize the ability of the platform to classify essays (Fig.  8 ). The bar graph revealed that the scores were polarized between the two datasets.

Simple bar graph comparing the mean originality scores for human-written and machine-generated texts in Crossplag Detector

The researcher also used SPSS to generate a histogram of originality scores classified by text type (Fig.  9 ). The histogram demonstrated that the distribution of originality scores was mainly polarized as the majority of human-generated essays received scores in the 99th percentile, and most machine-generated essays received scores within the 1st percentile. However, some of the scores for essays from both datasets varied widely and overlapped with the scores of the other dataset.

Histogram comparing the mean originality scores for human-written and machine-generated texts in Crossplag Detector

To measure the significance of the difference between the means scores of the two datasets, the researcher conducted a Mann–Whitney U test (Fig.  10 ). The Mann–Whitney U test revealed a significant difference between the human-written and machine-generated essays’ originality scores ( U  = 363, P  = 0.001) with a mean rank of 63.53 for the machine-generated dataset and 177.47 for the human-written dataset.

Mann–Whitney U test comparing the mean originality scores for human-written and machine-generated texts in Crossplag Detector

GPT Output Detector Demo and Crossplag Detector Performance Comparison

To compare the performance of both platforms in discriminating between machine-generated and human-written texts, the researcher conducted several analyses comparing the performance of both detectors on each data set. First, s/he used a clustered simple bar graph to visualize the difference between originality scores’ distributions for the machine-generated dataset across both platforms (Fig.  11 ). Visually, the originality scores distribution seemed consistent between the two platforms. However, Crossplag appeared to be more sensitive to machine-generated texts, as indicated by the higher concentration of scores close to 0% in the score distribution.

Clustered simple bar graph comparing the mean originality scores for machine-generated texts across the two detectors

To assess the significance of the difference in score distribution, the researcher conducted a Mann–Whitney U test between the originality scores of the machine-generated dataset in both platforms (Fig.  12 ). The Mann–Whitney U test revealed a significant difference between Crossplag Detector and GPT-2 Output Demo originality scores in favor of the latter ( U  = 1459, P  = 0.001) with a mean rank of 72.66 for Crossplag Detector and 168.34 for GPT-Output Detector Demo.

Mann–Whitney U test comparing the mean originality scores for machine-generated texts across the two detectors

Similarly, the researcher compared the originality scores assigned by both platforms to the human-written dataset visually and nonparametrically. The researcher used a clustered simple bar graph to visualize the difference between originality scores’ distributions for human-written texts in both platforms (Fig.  13 ). Visually, originality scores of GPT-2 Output Detector Demo appeared to be higher than those of Crossplag given that they appeared in high concentration in the 99.98% area while Crossplag scores were concentrated in the 99.00% area; however, the concentration of Crossplag Detector scores was higher than that of GPT-2 Output Detector Demo.

Clustered simple bar graph comparing the mean originality scores for human-written texts across the two detectors

To assess the significance of the difference in human-written dataset scores between both platforms, the researcher conducted a Mann–Whitney U test between the originality scores of human-written texts in both platforms (Fig.  14 ). The Mann–Whitney U test revealed a significant difference between Crossplag Detector and GPT-2 Output Detector Demo originality scores’ in favor of the latter ( U  = 2979, P  = 0.001) with a mean rank of 85.33 for Crossplag Detector and 155.68 for GPT-Output Detector demo.

Mann–Whitney U test comparing the mean originality scores for human-written texts across the two detectors

RQ#1: How effective is GPT-2 Output Detector Demo in detecting machine-generated essays?

Data analysis revealed that GPT-2 Output Detector Demo could discriminate between human-written and machine-generated texts, but its detection accuracy was inconsistent across the datasets. Most of the originality scores the detector assigned to human-written texts were concentrated in the 99th percentile with a median score of 99.98% and a narrow interquartile range of 0.41%. Based on a 95% confidence interval, the detector identified 86.7% of human-written texts as highly original, but it mis-evaluated 13.3% as possibly machine-generated (i.e., false negatives). As for machine-generated texts, the detector flagged most of the samples as potentially AI-generated. Most originality scores were concentrated in the 1st percentile, with a median score of 0.02% and a narrow interquartile range of 0.01%. Based on a 95% confidence interval, the detector identified 91.7% of machine-generated texts as unoriginal, but it mislabeled 8.3% of the samples as partially original (i.e., false positives). A Mann–Whitney U test comparing the originality scores of both machine-generated and human-written texts indicated that the difference between the scores assigned to both groups was significant, with a mean rank of 61.86 for the machine-generated essay group and 179.14 for the human-written essay group. However, the degree of variation in the scores of mis-evaluated samples (false negatives and false positives) varied widely, indicating that the detector can confuse machine-generated and human-written texts over some parameters. Thus, on average GPT-2 Output Detector Demo could discriminate between human-written and machine-generated texts with an 89.2% accuracy rate.

RQ#2: How effective is Crossplag Detector in identifying machine-generated essays?

Data analysis demonstrated that Crossplag Detector could differentiate between human-written and machine-generated texts, but detection accuracy was not maintained across the dataset. Similar to GPT-2 Output Detector Demo, Crossplag identified most of the human-written texts by assigning them originality scores in the 99th percentile with a median score of 99% and a tiny interquartile range of 0.00002%; however, the detector had a few lapses in judgment. Based on a 95% confidence interval, Crossplag identified 85% of human-written texts as highly original, but it mis-evaluated 15% as possibly machine-generated (i.e., false negatives). Regarding the machine-generated dataset, similar to GPT-2 Output Detector Demo, Crossplag flagged most machine-generated texts by assigning them originality scores close to 0% with a median score of 0.00001% and interquartile range of 0.00002%. However, it also misjudged a small portion of the dataset. Using a 95% confidence interval, the detector flagged 92.5% of machine-generated texts as unoriginal, but it mislabeled 7.5% of the samples as partially original (i.e., false positives). A Mann–Whitney U test comparing the originality scores of human-written and machine-generated datasets demonstrated a significant difference, with a mean rank of 63.53 for the machine-generated dataset and 177.47 for the human-written dataset. However, the degree of variation in the scores of mis-evaluated samples (false negatives and false positives) varied widely, indicating that the detector can confuse machine-generated and human-written texts over some parameters. In conclusion, Crossplag could classify texts as machine-generated or human-written with an average 88.75% accuracy rate.

RQ#3: What is the difference in classification effectiveness between GPT-2 Output Detector Demo and Crossplag Detector?

GPT-2 Output Detector Demo and Crossplag Detector achieved comparable classification accuracy, yet both detectors misjudged human-written and machine-generated texts over some parameters. While GPT-2 Output Detector Demo had an overall 89.2% average detection accuracy, Crossplag had an overall average detection accuracy of 88.75%. The detection accuracy for both platforms appeared to be similar, as the slight differences could be justified by random variability. Further investigation of detection accuracy between the two detectors based on text type (i.e., human-written or machine-generated) confirmed that both detectors had comparable levels of detection accuracy. It also demonstrated that each one was more sensitive to a specific text type. A Mann–Whitney U test comparing the originality scores assigned by both platforms for machine-generated texts indicated that GPT-2 Output Detector Demo scores were significantly higher than those of Crossplag with a mean rank of 72.66 for Crossplag Detector and a mean rank of 158.34 for GPT-2 Output Detector Demo. This finding indicates that Crossplag is more sensitive to the configurations of machine-generated texts and can detect them more accurately.

Conversely, another Mann–Whitney U test comparing the originality scores for human-written texts between both platforms revealed that originality scores for human-written texts were significantly higher for GPT-2 Output Detector Demo than for Crossplag Detector with a mean rank of 155.68 for GPT-2 Output Detector Demo and 85.33 for Crossplag Detector. This finding suggests that GPT-2 Output Detector is more sensitive to the configurations of human-written texts and less likely to produce false positives (i.e., misjudging human-written texts as unoriginal/machine-generated). Data analysis further revealed that both platforms misjudged the same text samples, either in the human-written or machine-generated dataset, suggesting that both platforms had similar challenges with specific configurations of text design that caused them to produce false negatives and false positives of comparable originality scores for the same essays. This finding is not unexpected given that both platforms share the same training data, GPT-2 model, and underlying detection mechanism, RoBERTa.

RQ#4: What does the comparison of two RoBERTa-based detectors suggest about the robustness of AI detection of AI-generated texts?

Data analysis demonstrated that RoBERTa-based classifiers could detect AI-generated texts with an average accuracy rate of 89% and underlined some vital considerations regarding the design and training of classifiers/detectors. Both experiments demonstrated that GPT-2-trained RoBERTa-based detectors could detect ChatGPT-generated texts. However, their detection accuracy was inconsistent across the dataset (see detailed explanations in RQ#1 and RQ#2). These results are consistent with Gao et al. ( 2023 ) findings about the effectiveness of GPT-2 Output Detector Demo in detecting ChatGPT-generated abstracts and OpenAI’s in-house experiments on using GPT-2-based detector to detect its generation (OpenAI, n.d. -b). The difference in accuracy rates compared to OpenAI’s study and the inconsistency of results in the current study are not unexpected, given the vast difference between GPT-2 model that was used to train the detector and GPT-3.5 model that powers ChatGPT. The two models vary in their training approaches and capacities (i.e., model size). While GPT-2 is a 1.5 billion parameter model (Solaiman et al., 2019 ) trained using in-context learning to achieve unsupervised multitask learning (Radford, 2019 ), GPT-3.5 is a 175 billion parameter model (OpenAI,  n.d. -a) trained using a combination of unsupervised machine learning and reinforcement learning from human feedback approaches to produce outputs aligned with users’ intents (Ouyang et al., 2022 ). Also, given that GPT-3.5 (the model that powers ChatGPT) is much bigger than GPT-2 that was used to train both detectors, these findings support OpenAI’s conclusion that the outputs of large models are more challenging to detect and that higher detection accuracy requires training on large models’ outputs (OpenAI, n.d. -b). These findings also support Solaiman et al. ( 2019 )’s conclusion that classifiers’ detection accuracy is deeply impacted by generative model size and that texts generated by larger models are more challenging to detect. Secondly, the relatively high accuracy of GPT-2-trained detectors in identifying the generations of a more developed version of GPT suggests that machine detection is more effective when language models detect their generations. This inference is consistent with Mitchell et al.’s ( 2023 ) conclusion that detection performance drops when the detection model differs from the generation model and that classifiers seem to be most effective in detecting their own generations. The high degree of accuracy of machine detection (89%), despite dealing with the generations of a more advanced version of the model, suggests that machine-generated texts have distinctive characteristics that machine classifiers can identify and use to detect AI-generated texts. This suggestion is consistent with Tay et al. ( 2020 ) finding that modeling choices leave traceable artifacts in the generated texts that could be used to identify the generative model and machine-generated texts. In addition, the high effectiveness of RoBERTa-based detectors supports the conclusions of Liu et al. ( 2019 ) and Solaiman et al. ( 2019 ) that the optimized training of RoBERTa model results in significant performance improvements compared to other detection models.

In summary, data analysis and interpretation revealed that both RoBERTa-based, GPT-trained detectors had a comparable performance and could discriminate between human-written and machine-generated texts with an average 89% accuracy. It is worth noting that each detector was more sensitive to a particular text type as GPT-2 Output Detector Demo detected machine-generated texts more accurately, while Crossplag Detector was more sensitive to human-written texts. These findings suggest that the output of larger models is more challenging to detect and that classifiers are more effective in detecting their own generations.

In this project, the researcher explored the potential of AI-based classifiers designed to detect AI-generated text as plagiarism detection tools that can help educators control the potential misuse of ChatGPT as a resource for AI-assisted plagiarism. Specifically, the researcher tested the performance of two GPT-2-trained, RoBERTa-based machine-detection classifiers in discriminating between human-written and AI-generated essays: GPT2-Output Detector Demo and Crossplag AI Content Detector. The purpose of the study has been to (a) assess the effectiveness of available AI-detection platforms to help L2 educators identify reliable platforms they can use to control AI-assisted plagiarism; (b) offer an in-depth investigation of this new, disruptive, and unexplored research territory to guide educators in dealing with AI-assisted plagiarism; and (c) evaluate underlying classifier type, design, and training mechanisms to contribute to the development of more robust machine-detection classifiers. To this end, the present descriptive study involved two experiments comparing the performance of two RoBERTA-based AI detectors on classifying a random set of texts as human-written or machine-generated. Each experiment involved comparing the originality scores assigned by a detector to 120 human-written essays and 120 ChatGPT-generated essays. Using Mann–Whitney U tests and Confusion Matrixes, the researcher compared the average score ranking of human-written and machine-generated essays for both detectors. Also, the researcher used Mann–Whitney U tests to compare the originality scores of both detectors for each text type. The study’s findings revealed that GPT-2 Output Detector Demo and Crossplag AI Content Detector achieved high accuracy in discriminating between machine-generated and human-written texts with an average accuracy of 89% despite being trained on a smaller and earlier version of GPT. Also, the findings indicated that GPT-2 Output Detector Demo was more sensitive to human-written texts and could detect them more accurately. At the same time, Crossplag Detector was more sensitive to machine-generated texts and had fewer false negatives. These findings demonstrate that AI-based classifiers could offer viable resources for detecting machine-generated texts and controlling AI-assisted plagiarism, even if they need further development to achieve higher detection accuracy. So, ironically, it seems that using the powerful capabilities of AI against itself can offer a viable resource for detecting AI texts, similar to the Terminator movie franchise when humans realized that a terminator machine would be a feasible protection against the machines’ attempts to assassinate the resistance leader.

Practical implications

The present study has several practical implications. First, as GPT Output and Crossplag detectors could identify AI texts with a relatively high degree of accuracy, L2 educators can rely on them to investigate potential cases of AI-assisted plagiarism. To overcome the limitations of each detector, it might be helpful to run a flagged text in one detector and then into another to double-check the results or upload the text to GLTR to get a visual report that can support initial results from an AI-based detector. Second, since available classifiers can misevaluate human-written texts as machine-generated (i.e., false positives), L2 educators should not base their decisions about academic integrity solely on detectors’ analysis and use other traditional approaches to ensure the academic integrity of students’ work. For instance, educators can interview students about the content of their work or analyze the linguistic complexity of flagged work compared to students’ in-class work. Third, since fine-tuned classifiers, especially RoBERTa-based classifiers, appear to be relatively effective in detecting AI-assisted plagiarism, major plagiarism detection platforms should integrate such classifiers into their systems rather than train their baseline classifiers from scratch. Fourth, as the findings demonstrate that model size substantially impacts the detection accuracy of classifiers, plagiarism detection platforms should train their detection classifiers on datasets from larger language models to improve their detection accuracy.

Limitations and future research directions

The present study is not without limitations. One of the study’s limitations is that the sample of human-generated essays was collected using convenience sampling and cannot be generalized to the population of ESL writing students. Also, the sample size was small, reducing the samples’ representativeness. Therefore, future studies can use larger sample sizes and collect the human sample using random sampling. Another limitation of the study is that the study compared only two detectors that are based on similar training protocols and model architecture. Perhaps future research could compare several detectors that use different training approaches and are based on different model architectures to compare the effects of these factors on detection accuracy. Another study limitation is that these AI essay samples were gathered in January 2023. Accordingly, the quality of essays generated could have improved slightly by the time of publication of the study due to the maturation of the AI system (neural network) due to continuous practice through interaction with users. Finally, the study’s results might not represent future generations of GPT. However, this is not likely to be the case for the recently released GPT-4 since it is based on the same model architecture and machine learning approaches as GPT-3.5. Its main improvements to its predecessor appear to be in processing multimodal context and cognitive processing (OpenAI, 2023), which are likely to have minor effects on the modeling processes of text generation that AI detectors use to identify AI texts (which is supported by the findings of the present study where classifiers trained on GPT-2 were able to detect GPT-3.5 outputs).

This study’s findings also suggest several future research directions. First, given that both detectors could identify texts generated by a later version of the LLM with relatively high accuracy, it is fair to assume that fine-tuning these detectors using GPT-3.5 data will likely boost detection accuracy. Thus, future research should investigate the effects of training classifiers on larger models on their detection accuracy. Second, since both RoBERTa-based detectors achieved relatively high and comparable levels of detection accuracy, it sounds reasonable to assume that RoBERTa model offers a robust architecture for classifier models. Therefore, future research should investigate the impact of different architecture, training, and modeling configurations on the detection accuracy of RoBERTa-based classifiers to optimize their performance further. Finally, given the anticipated vast proliferation and unprecedented disruptive impact of ChatGPT (and large language models in general) in ESL composition contexts, applied linguists should develop a new area of research in the field of intelligent CALL that explores the implications of generative AI for L2 learning and assessment, especially by drawing on recent advancement in NLP research to guide educators’ efforts to control AI-assisted plagiarism and integrate generative AI literacies in ESL classes. The present study is hoped to be a preliminary step in this direction.

Availability of data and materials

Data are available upon reasonable request.

Change history

31 october 2023.

A Correction to this paper has been published: https://doi.org/10.1186/s40468-023-00268-8

OpenAI released a newer version of their model, GPT-4, however, their popular and widely accessible platform ChatGPT is still powered by GPT-3.5 and GPT-4 is only limited to ChatGPT plus , which is a paid service that is only available to select users.

Abbreviations

Artificial intelligence

Analysis of variance

Bidirectional Encoder Representations

English as a second language

Giant Language Model Test Room

Generative Pre-trained Transformative m = Model

Large Language Model

Multiple Choice Question

Natural Language Processing

Robustly optimized BERT Approach

Statistical Package for the Social Sciences

Ali, W. Z. W., Ismail, H., & Cheat, T. T. (2012). Plagiarism: To what extent is it understood? Procedia - Social and Behavioral Sciences, 59 (2012), 604–611. https://doi.org/10.1016/j.sbspro.2012.09.320

Article   Google Scholar  

Baker, C. (2017). Quantitative research designs: Experimental, quasi-experimental, and descriptive. In H. Hall and L. Roussel (Eds.),  Evidence-based practice: An integrative approach to research, administration, and practice , (2 nd Ed., pp.155–183). Jones & Bartlett Learning.

Bakhtin, A., Gross, S., Ott, M., Deng, Y., Ranzato, M. A., & Szlam, A. (2019). Real or fake? learning to discriminate machine from human generated text.  arXiv preprint. arXiv:1906.03351 . https://doi.org/10.48550/arXiv.1906.03351

Bommarito II, M., & Katz, D. M. (2022). GPT Takes the Bar Exam.  arXiv preprint.  arXiv:2212.14402 . https://doi.org/10.48550/arXiv.2212.14402

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H Lin (Eds.), Advances in neural information processing systems: Vol. 33 , (pp.1877–1901). ISBN: 9781713829546. Retrieved June 30, 2023, from https://proceedings.neurips.cc/paper_files/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html?utm_medium=email&utm_source=transaction

Carr, D. (2023, June 14). As ChatGPT Growth Flattened in May, Google Bard Rose 187%. Similarweb . Retrieved June 30, 2023 from https://www.similarweb.com/blog/insights/ai-news/chatgpt-bard/

Chen, X., Ye, J., Zu, C., Xu, N., Zheng, R., Peng, M., ... & Huang, X. (2023). How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks.  arXiv preprint arXiv:2303.00293 . https://doi.org/10.48550/arXiv.2303.00293

Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett. Advances in Neural Information Processing Systems , 30 , 4299–4307. ISBN: 978151086096.

Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and Cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International . https://doi.org/10.1080/14703297.2023.2190148

Crossplag (n.d.). AI Content Detector. Retrieved June 30, 2023 from https://app.crossplag.com/

Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences, 340–341 , 250–261. https://doi.org/10.1016/j.ins.2016.01.033

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pretraining of deep bidirectional transformers for language understanding.  arXiv preprint arXiv:1810.04805 . https://doi.org/10.48550/arXiv.1810.04805

Hugging Face (n.d.). RoBERTa-base-OpenAI-detector. Retrieved June 30, 2023 from https://huggingface.co/roberta-base-openai-detector

Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021). TweepFake: About detecting deepfake tweets.  Plos one ,  16 (5). https://doi.org/10.1371/journal.pone.0251415

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27 (8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

Fireman Kramer, R. (1985). A overview of descriptive research. Journal of the Association of Pediatric Oncology Nurses, 2 (2), 41–45.

Francke, E., & Bennett, A. (2019). The Potential Influence of Artificial Intelligence on Plagiarism: A Higher Education Perspective. In P. Griffiths and M. N. Kabir (Eds.),  European Conference on the Impact of Artificial Intelligence and Robotics (ECIAIR 2019)  (pp. 131–140). Academic Conferences and Publishing Limited. DOI: https://doi.org/10.34190/ECIAIR.19.043

Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers.  Njp Digital Medicine , article 75. https://doi.org/10.1038/s41746-023-00819-6

Gehrmann, S., Strobelt, H., & Rush, A. M. (2019). GLTR: Statistical detection and visualization of generated text.  arXiv preprint arXiv:1906.04043 . https://doi.org/10.48550/arXiv.1906.04043

Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R. N., & Ahmad, H. (2022). "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data.  arXiv preprint arXiv:2212.05856 . https://doi.org/10.48550/arXiv.2212.05856

Hovy, D. (2016). The enemy in your own camp: How well can we detect statistically-generated fake reviews–an adversarial study. In  Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 351–356). Retrieved June 30, 2023 from https://aclanthology.org/P16-2057.pdf

Hu, K. (2023). ChatGPT sets record for fastest-growing user base-analyst note. Reuters , February 2, 2023. Retrieved June 30, 2023 from https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019). Automatic detection of generated text is easiest when humans are fooled.  arXiv preprint arXiv:1911.00650 . https://doi.org/10.48550/arXiv.1911.00650

Jawahar, G., Abdul-Mageed, M., & Lakshmanan, L. V. (2020). Automatic detection of machine generated text: A critical survey.  arXiv preprint arXiv:2011.01314 . https://doi.org/10.48550/arXiv.2011.01314

Johnson, A. (2023). ChatGPT in Schools: Here's Where It's banned-And How IT Could Potentially Help Students. Forbes , January 18, 2023. Retrieved June 30, 2023 from https://www.forbes.com/sites/ariannajohnson/2023/01/18/chatgpt-in-schools-heres-where-its-banned-and-how-it-could-potentially-help-students/?sh=3a758f366e2c

Khalil, M., & Er, E. (2023). Will ChatGPT get you caught? Rethinking of plagiarism detection.  arXiv preprint arXiv:2302.04335 . https://doi.org/10.48550/arXiv.2302.04335

King, M. R., & ChatGPT. (2023). Editorial: A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering , 16 (1), 1–2.

Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A watermark for large language models.  arXiv preprint arXiv:2301.10226 . https://doi.org/10.48550/arXiv.2301.10226

Lee, C., Panda, P., Srinivasan, G., & Roy, K. (2018). Training deep spiking convolutional neural networks with STDP-based unsupervised pretraining followed by supervised fine-tuning. Frontiers in Neuroscience, 12 , article 435. https://doi.org/10.3389/fnins.2018.00435

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach.  arXiv preprint arXiv:1907.11692 . https://doi.org/10.48550/arXiv.1907.11692

Lowie, W., & Seton, B. (2013). Essential statistics for applied linguistics . Bloomsbury Publishing.

Book   Google Scholar  

Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library Hi Tech News . ISSN: 0741–9058.

MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022). Generating diverse code explanations using the GPT-3 large language model. Proceedings of the ACM Conference on International Computing Education Research, 2 , 37–39. https://doi.org/10.1145/3501709.3544280

Mitchell, A. (2022) Professor catches student cheating with ChatGPT: 'I feel abject terror.' New York Post , December 26, 2022. Retrieved June 30, 2023 from https://nypost.com/2022/12/26/students-using-chatgpt-to-cheat-professor-warns/

Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023). DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. arXiv preprint arXiv:2301.11305 . https://doi.org/10.48550/arXiv.2301.11305 .

Nassaji, H. (2015). Qualitative and descriptive research: Data type versus data analysis. Language Teaching Research, 19 (2), 129–132. https://doi.org/10.1177/1362168815572747

OpenAI (n.d.-a) Documentation. Retrieved June 30, 2023 from https://platform.openai.com/docs/chatgpt-education

OpenAI (n.d.-b) GPT-2:1.5B release. Retrieved June 30, 2023 from https://openai.com/research/gpt-2-1-5b-release

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho and A. Oh (Eds.), Advances in Neural Information Processing Systems : Vol.   35 , (pp. 27730–27744). ISBN: 9781713871088.

Pan, W., Xiang, E., Liu, N., & Yang, Q. (2010). Transfer learning in collaborative filtering for sparsity reduction. In W. Pan, E. Xiang, N. Liu, and Q. Yiang (Eds.),  Proceedings of the AAAI conference on artificial intelligence  (Vol. 24, No. 1, pp. 230–235). https://doi.org/10.1609/aaai.v24i1.7578

Paul, R. (2005). The state of critical thinking today. New Directions for Community Colleges, 2005 (130), 27–38. https://doi.org/10.1002/cc.193

Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism and Mass Communication Educator, 78 (1), 84–93. https://doi.org/10.1177/10776958221149577

Pecorari, D., & Petrić, B. (2014). Plagiarism in second-language writing. Language Teaching, 47 (3), 269–302. https://doi.org/10.1017/S0261444814000056

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pretraining. Retrieved June 30, 2023 from https://www.mikecaptain.com/resources/pdf/GPT-1.pdf

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1 (8), 9.

Google Scholar  

Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., … & Wang, J. (2019). Release strategies and the social impacts of language models.  arXiv preprint arXiv:1908.09203 . https://doi.org/10.48550/arXiv.1908.09203

Susnjak, T. (2022). ChatGPT: The End of Online Exam Integrity?  arXiv preprint arXiv:2212.09292 . https://doi.org/10.48550/arXiv.2212.09292

Tay, Y., Bahri, D., Zheng, C., Brunk, C., Metzler, D., & Tomkins, A. (2020). Reverse engineering configurations of neural text generation models.  arXiv preprint arXiv:2004.06201 . https://doi.org/10.48550/arXiv.2004.06201

Turnitin (2023, March 16). Understanding the false positive rate for sentences of our AI writing detection capability. Turnitin. https://www.turnitin.com/blog/understanding-false-positives-within-our-ai-writing-detection-capabilities

Waltzer, T., & Dahl, A. (2023). Why do students cheat? Perceptions, evaluations, and motivations. Ethics and Behavior, 33 (2), 130–150. https://doi.org/10.1080/10508422.2022.2026775

Yang, M. (2023). New York City Schools ban AI chatbot that writes essays and answers prompts. The Guardian , January 6, 2023. Retrieved June 30, 2023 from https://www.theguardian.com/us-news/2023/jan/06/new-york-city-schools-ban-ai-chatbot-chatgpt

Yeadon, W., Inyang, O. O., Mizouri, A., Peach, A., & Testrow, C. (2022). The Death of the Short-Form Physics Essay in the Coming AI Revolution.  Physics Education, 58 (3). https://doi.org/10.1088/1361-6552/acc5cf

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alche’-Buc, E.Fox, and R. Garnett (Eds.). Advances in neural information processing systems, 32 , 1–12. ISBN: 9781713807933.

Download references

Acknowledgements

The author would like to thank the journal editor and reviewers for their feedback and support. The study was partially funding by a research grant from Gulf University for Science and Technology that covered the article processing charges.

The present study did not receive funding from any entity.

Author information

Authors and affiliations.

Gulf University for Science and Technology, Block 5, Building 1, Mubarak Al-Abdullah Area, West Mishref, Kuwait

Karim Ibrahim

You can also search for this author in PubMed   Google Scholar

Contributions

The paper was single authored by the corresponding author.

Corresponding author

Correspondence to Karim Ibrahim .

Ethics declarations

Competing interests.

The author declares no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original version of this article was revised: “The Acknowledgements section has been updated.”

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ibrahim, K. Using AI-based detectors to control AI-assisted plagiarism in ESL writing: “The Terminator Versus the Machines”. Lang Test Asia 13 , 46 (2023). https://doi.org/10.1186/s40468-023-00260-2

Download citation

Received : 01 August 2023

Accepted : 20 September 2023

Published : 16 October 2023

DOI : https://doi.org/10.1186/s40468-023-00260-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Generative AI
• Language assessment
• Academic integrity
• ESL composition

실시간 주요 뉴스 PUSH 알림서비스를 구독할 수 있습니다.

korea joongAng daily

Home > entertainment > k-pop.

ADOR CEO Min Hee-jin 'gathered evidence of NewJeans plagiarism by ILLIT, TWS and Riize'

Girl groups NewJeans, top, and ILLIT [ADOR, BELIFT LAB]

Related Article

What we know about the hybe-ador rift so far, exclusive: newjeans comeback will proceed as planned, despite hybe-ador drama, ‘hands off us’: documents reveal newjeans agency ador plotted against hybe's control, ador ceo slams hybe group illit as 'newjeans copycat'.

More in K-pop

Paul Kim announces marriage plans in letter to fans

Enhypen's Jay to release new single with Japanese rock band GLAY

'Sing Again' producers unveil new idol audition program 'Project 7'

As it happened — ADOR CEO Min Hee-jin press conference

Kim Yuna of Jaurim to release first solo album in eight years

Related Stories

ADOR's Min Hee-jin was coached by a shaman on company issues, HYBE says

HYBE to report ADOR CEO Min Hee-jin to police

ADOR CEO Min Hee-jin to hold press conference at 3 p.m.

HYBE-ADOR conflict: What will happen to NewJeans?

HYBE's ADOR label teases launch of first girl group

You might also like

To write comments, please log in to one of the accounts.

Standards Board Policy (0/250자)