students plagiarism in all of their assignments

Number responding

17,000

71,300

% who admit cheating on tests:

17%

39%

% who admit cheating on written assignments:

40%

62%

% total who admit written or test cheating:

43%

68%

Academic Honesty Contract

One of the fundamental principles of this university is that “academic honesty and integrity are fundamental to the mission of higher education and of the University of Wisconsin system” (Wisconsin Administrative Code 14.01). While what constitutes in-class cheating (copying the work of others, unauthorized use of prepared notes, etc.) is often obvious to students, plagiarism merits further elaboration.

Plagiarism is…

×        Using someone else’s words or ideas without proper documentation.

×        Copying some portion of your text from another source without proper acknowledgement of indebtedness.

×        Borrowing another person’s specific ideas without documenting their source.

×        Having another person correct or revise your work. This differs from getting feedback from a writing group, or from an individual, which you then attempt to implement.

×        Turning in a paper written by another person, from an essay “service,” or from a website (including reproductions of such essays or papers).

In addition to the instruction you have received in this course, writing handbooks are excellent sources for learning how to avoid plagiarism. The writing center has an online handbook that can be accessed at www.wisc.edu/writing. Click on “Writer’s Handbook.” And of course, you may always talk with me if you have any questions about plagiarism.

Consequences:

Anyone who plagiarizes in this class will be reported to the Director of Composition and earn a failing grade in the course. Further penalties may include suspension or expulsion from the University.

Signing below indicates:

×        I understand what plagiarism is,

×        I will ask my instructor if I have questions regarding plagiarism,

×        I understand my responsibilities regarding this matter, and

×        I agree to abide by the above consequences should I intentionally plagiarize.

_______________________ (student) _________ (date)          __________________________ (instructor) ________ (date)

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Everything College Students Need to Know About Plagiarism

It’s a big deal to get caught plagiarizing. Some students see copy-paste as a time-saving shortcut for writing lengthy papers, but it’s more like a shortcut to:

• Failing the assignment.
• Getting kicked out of a class.
• Getting kicked out of college.
• Receiving a formal reprimand.
• Destroying your academic reputation.
• Fighting a lawsuit.

Almost all schools have their own policies about what constitutes plagiarism, and how it should be dealt with. Unless you’re plagiarizing your professor, you’re probably not going to wind up in court. But the other four consequences are definitely on the table.

You’re here because you want to avoid plagiarizing someone else’s work. So we’re going to look at what is and isn’t plagiarism, and what to do if you’ve committed it.

For starters, let’s look at what you’re trying to avoid.

What is plagiarism?

Plagiarism is taking someone else’s work and presenting it as your own. That last part—presenting it as your own—is what makes plagiarism inherently wrong and sets it apart from a quote or reference. In academic writing, you’ll often refer to the work of scholars and quote experts, but if you try to pass their work off as yours, it’s stealing.

And it’s not just a matter of changing the words you use. Plagiarism covers both words and ideas . Which means if you got an idea from someone else, you can’t present it as your own, even if you use your own words.

At the same time, Turnitin—a plagiarism software company—says that the Internet “has created an environment that encourages information sharing and values the remixing and remaking of original content. In this environment, plagiarism is easier to commit and originality more difficult to define.”

Avoiding plagiarism might sound like navigating a minefield—how do you make sure no one has ever said something similar to what you put in your paper? But that’s not why plagiarism is so prevalent. Most students who plagiarize do it on purpose.

How students commit plagiarism

According to Turnitin, the most egregious form of plagiarism is also the most popular method: copy-pasting an entire paper. As ridiculous as it seems, after surveying thousands of actual examples of plagiarism , Turnitin found that many students “write” papers without writing a word.

There’s simply no excuse for this. There’s no way to accidentally copy an entire paper word-for-word. Students do this because they don’t think they’ll get caught, not because they think it’s OK. If a student gets caught blatantly plagiarizing like that, there’s not a lot of room for leniency. It’s a textbook case of academic dishonesty.

The next most common methods aren’t much better. The second most popular way students plagiarize is what Turnitin calls a “mashup”: copying passages from several sources and splicing them together without citations.

The types of plagiarism professors typically encounter are derivatives of this kind of copying. On the most innocent side of the plagiarism spectrum, you have students who properly cite works but closely followed the source’s original wording or structure. Or relied so heavily on cited material that there was pretty much nothing original in the paper.

Whether the actual words are different or not, all forms of plagiarism have one thing in common: little or no original thought.

Still, not all plagiarizers do it on purpose.

Can you plagiarize something on accident?

Nobody accidentally walks into your house and takes your wallet. And nobody accidentally copy-pastes an entire paper, puts their name on it, and turns it in. Still, it is possible to accidentally plagiarize.

Not all plagiarism is as obvious or concerning to colleges as copy-pasting (according to Turnitin). Some people plagiarize because they thought what they were doing was OK, not because they thought they were getting away with something. It’s not an excuse, but colleges may be a little more merciful towards accidental plagiarism.

Sharon Greenthal of The Spruce says, “Students inadvertently—even subconsciously—use the same words they have read because they are unable to interpret what they have learned and explain it with their own thoughts and sentences.” Especially when you’re fatigued and stressed, it’s possible to study a paragraph and then borrow too heavily from it without realizing what you’re doing.

Other ways to accidentally plagiarize basically come down to someone thinking they’ve “changed enough” of the source to make it an original thought. Or properly citing the source but contributing too little original material.

But putting something into your own words isn’t enough. If most of your paper is the words of other people, you haven’t really “written” it, have you? This is still plagiarism. But if you just hit send or handed in a paper where you did something like that, you might be OK. The “I didn’t know” card won’t salvage your academic reputation, but you might get off with little worse than an “F” on the assignment. Assuming, of course, that this is your first time, you own up to it, and you never, ever do it again. (But even then, the consequences may still be more severe.)

You can also plagiarize yourself if you borrow from previous papers without citing them. Even if you give yourself permission to use your work, it’s academic dishonesty. You had a source, and you didn’t cite it. I know that sounds silly, but in Turnitin’s list of the top 10 most common types of plagiarism, “recycling” old papers was #5. It’s not as concerning as stealing someone else’s work, and it’s pretty hard to get caught unless you have the same professor for multiple classes, but schools take this seriously.

So how do you avoid this situation altogether? It comes down to two things: citing your sources and producing original ideas.

How do you avoid plagiarism?

In anything you write, copied words should only ever take one form: quotes. Copied ideas should only ever take one form: references. If someone said something amazing that proves a point you’re making, quote them. If someone inspired your line of thinking, say so. Avoiding plagiarism doesn’t force you to work in a bubble. But there’s a right way to use someone else’s work, and a wrong way.

The “right way” varies depending on the style guide your school uses, such as MLA , APA , or CMOS . But those differences are more about dotting your i’s and crossing your t’s. The difference between plagiarism and a quote or a reference generally comes down to this: are you in any way, shape, or form trying to suggest that someone else’s work is yours? Or for the accidental plagiarizers: does it appear that you’re trying to suggest that someone else’s work is yours?

It’s easy to get stressed out about how to follow a style guide (especially if different classes require different style guides). Some professors will seriously lower your grade for what seems like a trivial formatting mistake. But your fear of complicated style guide conventions isn’t an excuse for plagiarism. If you need to, use more informal references in your first draft, and figure out the exact formatting when you’re revising. (I’ve been using super informal references all throughout this article, by the way.) The point is just to make sure you’re giving credit where credit is due.

A good quote either functions as a capstone or a launchpad for your own ideas. It helps you pivot from one thought to the next. If you’re only using quotes to beef up your word count or reach the page limit, it’s not plagiarism, but if you do it enough, it can be.

Your paper has to balance quoted or referenced material with original content. You can’t just Frankenstein a bunch of quotes together and call it good. Your thoughts and your ideas have to be the driving force behind the paper. Otherwise even with proper citations, you’re still just putting your name on other people’s work.

If you’re still worried about plagiarizing, run your essay through a plagiarism detection software such as Viper or Grammarly . It may cost you a couple bucks, but it’ll tell you how much (if any) of your essay appears in other published works, and it’s the same way your professors check for plagiarism.

(You may also want to check out these helpful study apps for college students .)

What if someone else wrote my paper for me?

If you pay someone to write your essay, technically, this is still plagiarism—you’re just a lot less likely to get caught. This is obviously cheating. You were asked to write the essay, and you didn’t write it, but you put your name on it. A ghostwriter isn’t a loophole.

But this isn’t just a problem for the student who submits the plagiarized paper. Now there’s an accomplice. Many schools will punish the ghostwriter as well (assuming they can). This ultimately comes down to a school’s academic dishonesty or plagiarism policy, which probably, your professors have talked to your class about at some point. They don’t like to leave wiggle room for the “I didn’t know” card.

Here are some things a plagiarism policy might include:

Consequences for plagiarism

We’ve already talked about the main consequences a school may employ:

• A failing grade on the assignment.
• A failing grade in the class.
• A formal reprimand.
• Permanent expulsion from the school.
• Academic probation.
• A poor academic reputation.

While Turnitin suggests that plagiarism exists on a scale, colleges don’t necessarily treat it that way.

“Academic policies too often take the approach of adopting a one size fits all response to plagiarism,” Turnitin says. “This has led to policies that tend to be too extreme and bureaucratic (the latter reflecting the pressure of needing to justify extreme responses).”

But while a school’s official policy may include dismissal from the college, it’s often up to the person who catches an instance of plagiarism to decide what to do with it. Turnitin believes that a student’s intent matters, and that the method of plagiarism can give faculty clues as to someone’s intent.

Denial of blatant plagiarism is pretty much a dead giveaway that a student knowingly committed it.

What do I do if I’ve committed plagiarism?

It’s totally possible that you weren’t sure you committed plagiarism until just now. But even if you already knew you plagiarized, and you did it on purpose, the next step is the same: own it before you get caught.

Especially if you plagiarized on purpose, you’re probably hoping this will all go unnoticed. But the consequences for plagiarizing are too steep and the rewards are too small for that gamble to be worth it.

Plagiarism used to be pretty hard to spot. If a paper looked suspicious—the writing didn’t sound like the student or had an inconsistent voice—professors had to identify plagiarized passages manually or ask the student enough questions to prove it wasn’t their work.

Today there’s an entire industry of plagiarism detection software. Professors have multiple brands to choose from when they want to automatically check students’ work for plagiarism.

But even without software, professors can get pretty good at recognizing the most obvious forms of plagiarism. Class discussions and assignments allow them to learn how individual students think. And especially in small classes, they learn to recognize the way those students write. Plus, as professors, it’s pretty safe to assume they’re very familiar with the most common sources.

If you plagiarize, odds are your professors will find out. Get ahead of the problem, and tell them you made a mistake. Don’t lie about this or wait to see what happens.

If it was a small instance of plagiarism, pointing out an uncited reference may be all it takes to prevent backlash. But at the very least, telling the truth will start the conversation about consequences on the right foot. It can’t undo the damage of your academic dishonesty, but it being upfront about it will help rebuild trust for your future work as well.

All you really need to know about plagiarism

When it comes down to it, avoiding plagiarism takes two things:

• Cite your sources.
• Produce original work.

As long as you do that, you shouldn’t have anything to worry about.

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Thanks for a succinct guide to what students need to know about plagiarism. I am a high school principal and plan to share this website with a student who we recently caught plagiarizing.

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A publication of the harvard college writing program.

Harvard Guide to Using Sources 

• The Honor Code
• Harvard University Plagiarism Policy

Plagiarism and Collaboration

The College recognizes that the open exchange of ideas plays a vital role in the academic endeavor, as often it is only through discussion with others that one is fully able to process information or to crystallize an elusive concept. Therefore, students generally are encouraged to engage in conversations with their teachers and classmates about their courses, their research, and even their assignments. These kinds of discussions and debates in some ways represent the essence of life in an academic community. And yet, it is important for all scholars to acknowledge clearly when they have relied upon or incorporated the work of others. To ensure the proper use of sources while at the same time recognizing and preserving the importance of the academic dialogue, the Faculty of Arts and Sciences adopted the following policy, which you can also find in the Student Handbook.

It is expected that all homework assignments, projects, lab reports, papers, theses, and examinations and any other work submitted for academic credit will be the student’s own. Students should always take great care to distinguish their own ideas and knowledge from information derived from sources. The term “sources” includes not only primary and secondary material published in print or online, but also information and opinions gained directly from other people. Quotations must be placed properly within quotation marks and must be cited fully. In addition, all paraphrased material must be acknowledged completely. Whenever ideas or facts are derived from a student’s reading and research or from a student’s own writings, the sources must be indicated (see also “Submission of the Same Work to More Than One Course” below.)

Students must also comply with the policy on collaboration established for each course, as set forth in the course syllabus or on the course website. Policies vary among the many fields and disciplines in the College, and may even vary for particular assignments within a course. Unless otherwise stated on the syllabus or website, when collaboration is permitted within a course students must acknowledge any collaboration and its extent in all submitted work; however, students need not acknowledge discussion with others of general approaches to the assignment or assistance with proofreading. If the syllabus or website does not include a policy on collaboration, students may assume that collaboration in the completion of assignments is permitted. Collaboration in the completion of examinations is always prohibited.

The responsibility for learning the proper forms of citation lies with the individual student. Students are expected to be familiar with the Harvard Guide to Using Sources. Students who are in any doubt about the preparation of academic work should consult their instructor and Resident Dean before the work is prepared or submitted.

Students who, for whatever reason, submit work either not their own or without clear attribution to its sources will be subject to disciplinary action, up to and including requirement to withdraw from the College. Students who have been found responsible for any violation of these standards will not be permitted to submit course evaluation of the course in which the infraction occurred.

Submission of the Same Work to More Than One Course

It is the expectation of every course that all work submitted for a course or for any other academic purpose will have been done solely for that course or for that purpose. If the same or similar work is to be submitted to any other course or used for any other academic purpose within the College, the prior written permission of the instructor must be obtained. If the same or similar work is to be submitted to more than one course or used for more than one academic purpose within the College during the same term, the prior written permission of all instructors involved must be obtained. A student who submits the same or similar work to more than one course or for more than one academic purpose within the College without such prior permission is subject to disciplinary action, up to and including requirement to withdraw from the College.

Students are urged to consult their Resident Dean or the instructors involved with questions concerning this important matter (see also “Plagiarism and Collaboration” above).

Tutoring Schools and Term Paper Companies

In keeping with the principle that all material submitted to a course should be the student’s own work, any undergraduate who makes use of the services of a commercial tutoring school or term paper company is liable to disciplinary action. Students who sell lecture or reading notes, papers, or translations, or who are employed by a tutoring school or term paper company, are similarly liable and may be subject to disciplinary action, up to and including requirement to withdraw from the College. If a student wishes to accept compensation for private tutoring in Harvard courses, prior written permission of the Dean of the College is required.

• What Constitutes Plagiarism?
• The Exception: Common Knowledge
• Other Scenarios to Avoid
• Why Does it Matter if You Plagiarize?
• How to Avoid Plagiarism

PDFs for This Section

• Avoiding Plagiarism
• Online Library and Citation Tools

• Current Students

Office of the Dean of Students

The common types of plagiarism, direct plagiarism .

Direct plagiarism is the word-for-word transcription of a section of someone else’s work, without attribution and without quotation marks. The deliberate plagiarism of someone else's work is unethical, academically dishonest, and grounds for disciplinary actions, including expulsion. [ See examples .]

Self Plagiarism

Self-plagiarism occurs when a student submits his or her own previous work, or mixes parts of previous works, without permission from  all  professors involved. For example, it would be unacceptable to incorporate part of a term paper you wrote in high school into a paper assigned in a college course. Self-plagiarism also applies to submitting the same piece of work for assignments in different classes without previous permission from  both  professors.

Mosaic Plagiarism

Mosaic Plagiarism occurs when a student borrows phrases from a source without using quotation marks, or finds synonyms for the author’s language while keeping to the same general structure and meaning of the original. Sometimes called “patch writing,” this kind of paraphrasing,  whether intentional or not , is academically dishonest and punishable – even if you footnote your source! [ See examples. ]

Accidental Plagiarism

Accidental plagiarism occurs when a person neglects to cite their sources, or misquotes their sources, or unintentionally paraphrases a source by using similar words, groups of words, and/or sentence structure without attribution. ( See example  for mosaic plagiarism.) Students must learn how to cite their sources and to take careful and accurate notes when doing research. (See the section on note-taking section within the Prevention of Plagiarism in College website which is also highlighted on the Writing Resources page by the Baldwin Center for Learning and Teaching .) Lack of intent does not absolve the student of responsibility for plagiarism. Cases of accidental plagiarism are taken as seriously as any other plagiarism and are subject to the same range of consequences as other types of plagiarism.

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Why Do Students Commit Plagiarism?

With everything at risk... why do students choose to plagiarize.

By now, most students know that plagiarism is wrong. Though many still lack the necessary skills to incorporate the works of others and cite them, most understand you shouldn’t hire an essay mill or copy and paste wholesale from the internet.

So why do so many students do it?

Even as the punishments for plagiarism get more and more strict and the tools for spotting it keep improving. In short, the risks in committing plagiarism keep rising, the theoretical rewards stay the same but there is little change in behavior.

It can seem odd, but the truth is that we know why students plagiarize. Over the past decades study after study have highlighted the reasons students have given for plagiarism and, though the specific order may change, the same causes remain at the top.

If we set aside accidental plagiarism and students that lack the skills to cite properly (that is a completely separate topic for another day), there are really three core reasons that students commit plagiarism. In no particular order, those reasons are as follows:

Simply put, students may not feel motivated to actually complete an assignment. This could be because they don’t believe their education is valuable, that the course is worthwhile or that the assignment is meaningful. It could even be as deep as not believing that citation itself is important due to the commodification of information.

In studies, this often presented as laziness or wanting to take the most efficient route through an assignment. However, it really comes down to student apathy. If students believe that the thing they are working for is valuable and that the process is meaningful, they will at least want to put in the work.

Apathy is a difficult problem to crack because we can’t control the attitude that students have when they come into the classroom. If they were forced to go to school or the course is one they aren’t interested in but are required to take, it’s difficult to motivate them to care.

Teachers are limited here. They can give more interesting and less generic assignments, but making someone care about something they aren’t interested in is nearly impossible. For many students, apathy is their biggest enemy.

Ultimately though, these are the students most targeted by increased plagiarism enforcement. The hope is that by increasing the risk, schools can motivate them to care. However, that doesn’t seem to work as few students still expect to be caught.

At the other end of the spectrum is fear.

Students are under ever-increasing pressure to do more. They are under intense pressure to keep up their grades as their scholarships, jobs and even their standing in school are all tied to them. However, not all students are strong writers and, even those that are may lack confidence in their skills.

When a student needs a certain grade but doesn’t feel confident that they can do it honestly, cheating becomes much more tempting.

While it sounds crazy, there’s a logic to it. If the student feels that they’re going to fail they are, in their mind, already dead. Plagiarism, though seriously risky, makes them less dead by giving them at least a chance of passing and slipping by undetected.

These students typically don’t want to cheat. They feel forced into it through external factors.

Here, schools and instructions need to find ways to mitigate fears. This can take a wide variety of forms including offering remedial writing courses, promoting student success centers that teach the needed skills and even offering ways for students that are struggling to get remedial help without harming their grade point averages.

The problem is that the students most in need may not ask for help simply because it can be difficult to admit you need it. As such, schools need to be as proactive as possible in seeking these students out.

3: Lack of Resources

Finally, there are times plagiarism isn’t caused by fear or apathy, but by a lack of the needed resources. However, unlike accidental plagiarism, which is caused by a lack of study skills or knowledge about attribution rules, this looks other kinds of resources students need to complete an assignment.

This includes personal elements such as the needed time and energy and focus as well as access to physical things such as libraries, computer labs and the internet.

Students who do actually believe in the importance of the assignment and are confident they can pass may lose that confidence if they find themselves up against a wall when trying to complete it. If there is inadequate time, mental energy or they are missing the physical resources that they need to complete it, they may feel forced into cheating.

Often times, this is very much the student’s fault. In nearly every study on why students plagiarize lack of time management is one of the top causes. Other times, it might be more complicated. If a student is working full time and/or raising children, there may simply not be enough hours in the day to complete some assignments. Even if there is enough time, there may not be enough energy or focus without risking their mental or physical health.

Likewise, students may be missing another resource. As much of the world moves to distance learning due to COVID-19, students with limited internet access are going to struggle with research. Even students who are on campus may not be able to go to the library or computer labs during opening hours.

Even if students believe in the assignment and are confident they have the needed skills, they may still be tempted to plagiarize if they don’t feel they have the needed resources.

Bottom Line

If you want to make a student that almost never plagiarizes, you simply need a student that’s passionate about the assignments, is confident in their skills and has the resources to complete the tasks they are given.

It sounds like a simple formula but it is far from it. We can’t force students to care, identifying students that lack confidence can be difficult and the resources a student needs are often well outside the school’s control.

Still, this points to a strategy that goes beyond just ramping up enforcement. Working on reducing apathy and fear while improving access to resources can do as much to reduce plagiarism as any honor code or any strict disciplinary approach to plagiarism.

Teachers are there to teach students, not threaten and punish them. If there are ways teachers can reduce plagiarism and help students improve, that is a true win-win for everyone involved.

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Plagiarism Checker for Students

Quetext's plagiarism checker is a must-have free tool for every student. Identify plagiarism before it's too late, get resources to improve your writing, and add citations quickly, all for free. You do the writing, and we'll help with the rest.

Why use Quetext over other plagiarism detection tools?

Avoid “accidental plagiarism”.

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.

Cite sources directly in your paper

For the copy you want to keep, 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.

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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:

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How you can avoid plagiarizing

Know the different types of plagiarism

There are several types of plagiarism, and it’s essential to understand the differences to avoid making these mistakes and prevent plagiarism in the classroom.

• Direct plagiarism: Using someone else’s exact words and using them as your own with no credit to the source. Eliminating portions of the text still qualifies as direct plagiarism.
• Patchwork plagiarism: Creating new text by combining exact phrases or sentences from multiple sources without acknowledging the original authors or sources.
• Self-plagiarism: Attempting to reuse or recycle your previous work and claiming it as a new piece of work without mentioning the source of original publication.
• Source-based plagiarism: Producing false sources within your writing or omitting one or more references from citations.

Cite your sources

Students often think of citations as a simple means of acknowledging another author's work. While this is their main purpose, there are other benefits as well.

Source citations also show readers where to go to learn more. By providing context around the citation in student papers, such as the source medium and when it was published, readers can locate the information more easily. Giving too little source information or improperly formatting it can result in unintentional plagiarism. These mistakes also make it difficult for readers to locate the original source and verify information.

Quetext's citation generator makes it easy for students to accurately cite their sources without slowing down their writing process. Once a user inputs the text, the software identifies the passages that require a citation and subsequently generates it in the style required — APA, MLA, or Chicago.

Always use a plagiarism checker

The best way to avoid severe plagiarism consequences is to use a plagiarism checker. Often, plagiarism is unintentional and results from poor paraphrasing of material found online. Using a plagiarism checker, the student can avoid accusations of any kind of plagiarism and benefit from a close review of the original material.

Quetext's plagiarism checker provides a speedy, easy process for students to receive accurate results, identifying areas of concern and providing on-page solutions.

Plagiarism Checker for Students FAQ

What are the consequences if a student is caught plagiarizing.

The consequences of plagiarism vary according to the student’s academic institution and grade level. For example, a middle school student who plagiarizes within an essay may receive a stern warning and additional instruction related to the process of citing sources or summarizing one’s thoughts.

However, students in higher education institutions guilty of plagiarism in academic papers face much steeper repercussions, including, but not limited, to failing grades and disciplinary or legal action. Factors such as the assignment itself, the instructor, and whether or not it is your first offense impact the outcome.

Depending on the severity of the situation, plagiarism can also impact a student’s reputation or future career opportunities.

How does a plagiarism checker help students?

A plagiarism checker helps students be proactive in finding instances of plagiarism before an assignment is completed and turned in with no way to correct the errors.

In addition, by identifying the potential conflicts early, students can conduct further research to find alternative sources to understand the material better and revise or rewrite the offending passages.

Plagiarism checkers also help students learn innovative writing techniques, improve their writing skills through better research and experience in summarizing ideas, and preserve academic integrity.

Do teachers use plagiarism checkers?

Many teachers use plagiarism checkers to ensure a student’s submission is 100% original work. With the rise of easy-to-use tools like Quetext, checking for authenticity and text matches is a quick and affordable process to scan millions of online data sources.

Using plagiarism checkers also allows teachers to educate their students on the importance of citing sources and the ethical responsibilities of research and opinion writing.

Plagiarism has always been a source of concern for teachers, but until now, options for manual detection have limited their opportunities to identify the problem efficiently. However, the evolution of technology in this area has made the tools more accessible than ever.

How do teachers check a paper for plagiarism?

Teachers are skilled at detecting plagiarism, especially those who have been teaching for a significant period or who teach in the areas of English or Literature. Some teachers use a simple technique of entering a sentence into a search engine to find instances of duplicate text on web pages.

However, this manner of plagiarism detection is manually intensive and dependent upon unreliable algorithms. Some may use their writer’s intuition by identifying anomalies in sentence structure, inconsistencies in vocabulary or grammar, or a combination of all of these.

Others use more sophisticated and robust plagiarism detection software, like Quetext. Quetext can scour millions of sites using DeepSearch™ technology to identify plagiarism in seconds. These tools also enable instructors to work with students to improve their writing and avoid repeating the same mistakes in the future.

Can students get in trouble for accidental plagiarism?

Yes, students can be penalized for plagiarism, even if it was an accident. Unfortunately, instructors have no way of knowing the intent behind plagiarism. Depending on the type of plagiarism, instructors may allow leeway for students to learn from their mistakes and correct the work without the threat of severe consequences.

However, repeat offenders are less likely to receive this flexibility and may face steeper penalties. Additionally, instructors of college-level courses with clear plagiarism guidelines and policies may be less inclined to let students off the hook and instead enforce severe consequences.

Is there a free plagiarism checker for students?

Yes! Quetext offers both a free and a paid subscription for students to seamlessly verify their writing. Quetext goes beyond traditional plagiarism checkers to provide students with advanced technology that puts content through three layers of evaluation in just a matter of seconds. This level of insight enables students to understand the degree to which the text has been plagiarized and decide whether to rewrite the text or use the citation assistant to cite sources.

Quetext also offers a detailed report students can use to document their editing efforts and present as proof of a plagiarism-free, 100% original content document.

Quetext's plagiarism tool also offers flexible pricing for its Pro features which facilitates the citation of online sources, attributions, and real-time feedback for writing improvement.

How can a student check for plagiarism?

Anyone can enter the text from a research paper into a search engine and find potential matches that identify instances of plagiarized content or duplicate content. However, this method is largely ineffective and does not identify every area of concern.

Students can use Quetext's free online plagiarism checker by creating an account, copying and pasting the text in question into the plagiarism checker, and receiving a comprehensive analysis with color-coded feedback, a plagiarism percentage score, and options for editing the text.

Quetext is one of the best plagiarism checkers available with high-quality insights and accurate results.

Education Technology for Digital Assessments, Exams, Admissions and trends

Introduction of Online Exams, Digital Admission System , Education Technology Trends

Top reasons why students present plagiarized assignment

Assignments and examinations have been part of the educational process from the very start. That means, so are cheating and plagiarizing.

Every institute defines plagiarism differently. It has serious consequences in educational institutes and is strictly prohibited.

Plagiarism means copying or stealing the work of others or not giving them credit for it. It can be intentional or accidental.

There are many reasons why students tend to plagiarize but the top 6 reasons are listed below. Teachers can take measurements to reduce the percentage of plagiarized assignments and exams.

Article Contents

1. Overconfidence

When a student gets away with plagiarism one time or more, he or she would think they are clever enough to trick the teacher. They become overconfident and plagiarize every time they can.

This happens when the invigilator or teacher is not paying attention. They skim read assignments. As a result, students are not caught if they plagiarize.

The reasons why the teacher cannot detect plagiarism can be laziness, no familiarity with the plagiarism or sometimes it can be by accident. How to prevent it?

Firstly, teachers need to tell the consequences of plagiarism to students strictly. That there will be no leniency.

Warn them about plagiarism over and over again. If possible, give students references to such incidents in your institute so they will get lessons. Every teacher should perform their duty diligently. Check all the assignments deeply.

Use a  plagiarism checker . This way students will get away with copying

2. Poor time management and un-organization

The next reason on this list is also very common to witness. Students procrastinate their assignments and think they will complete them tomorrow and this goes on.

They spend their time on useless hobbies and when the deadline arrives near, they panic. This causes them to plagiarize. Since they do not have enough time, they copy from others.

Sometimes they do add citations but the amount of quotes is so large that it becomes clear students put no effort into it.

Another reason is unorganized notes. Students make poor or no notes and when they finally start to work on the assignment, they make blunders. Most commonly, students add improper citations.

How to prevent it?

Students should be taught time management skills and make the most out of them. Making timetables can be helpful. To-do lists are also very common among students when it comes to time management.

While searching, students should make organized notes with proper citations. For the speedy process, the student can use an online wordpad for simultaneous research and note-taking.

3. Laziness

One of the most common reasons due to which students plagiarize is laziness. This happens due to the lack of productivity. Students know they can copy and do not work hard.

Because of laziness, they sometimes plagiarize their peers’ assignments and sometimes their own. It is unfair to those students who work hard.

Teachers should teach their students about the importance of hard work. Along with that, students should know how false the act of plagiarism is.

Students can follow productivity tips and use online tools to tackle laziness.

4. Lack of knowledge about plagiarism

Another reason why students plagiarize is that they do not know the importance of unique assignments and they are not exquisitely knowledgeable about plagiarism.

Because of this, students don’t consider plagiarism unethical. Or in the case of lack of knowledge, they don’t give much importance to citation.

As said earlier, educators should guide students about the institute’s policy about plagiarism. Some institutes consider every type of plagiarism a crime whether it’s intentional or unintentional.

In that case, students should proofread their assignments and exam papers before final submission. A plagiarism checker can be used to remove any chance of accidental plagiarism.

5. Online Learning Trends

After December 2019, when the covid pandemic started to spread on earth, the trend of online learning reached its heights. Almost all the institutes around the globe have shifted to remote institutes.

Where there are many advantages of online learning, its disadvantages cannot be ignored. The most highlighted one is plagiarism in online exams and assignments.

It is seen that students of the same course sit together and take online exams. They help each other or in other words cheat. Similarly, when students spend so much time on the net, they also use it to write assignments.

Which is not necessarily a bad thing if done rightly. But many students take it lightly and don’t pay attention to plagiarism and citation.

Since online learning is inevitable now, institutes will have to adopt measures to make online learning efficient.

To avoid the cheating process, modern technology should be used. Students should be monitored through webcams. Mics must be demanded to be turned on. To guard this process further, secure browser technology can be implemented.

6. Lack of Motivation

When new types of projects are assigned to students without any guidelines, they look for shortcuts. Students think as it is a new topic, it would be alright to copy.

They think it is obligatory to submit an assignment with lots of facts and experts’ words. They keep their ideas to themselves and fear to open up. Such students have no thinking of their own.

This happens mostly when a divergent topic is assigned to them. learners have no knowledge about it and search online. In doing so, they cite the expert words only and submit an over-cited assignment.

This way the assignment consists of material that has already been presented. It contains nothing new. Students do not think about the topic.

Like most cases, students need to be taught some lessons in this case also. Teachers should motivate students to present their own ideas. Discussion and brainstorming sessions are good techniques to boost confidence.

The toughest job in this process is of the educators. They need to monitor and track the progress and habits of each and every student.

Students should be taught about the importance of thinking by themselves. If they plagiarize now and get away with it, it should be kept in mind it will not happen every time.

They may get good grades by doing this, but they will not have any knowledge. This will get them in trouble in the future. Because it is not numbers that matter in the end, it is experience and knowledge.

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• Published: 06 August 2024

AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI

• Attila Dabis   ORCID: orcid.org/0000-0003-4924-7664 1 &
• Csaba Csáki   ORCID: orcid.org/0000-0002-8245-1002 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1006 ( 2024 ) Cite this article

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This article addresses the ethical challenges posed by generative artificial intelligence (AI) tools in higher education and explores the first responses of universities to these challenges globally. Drawing on five key international documents from the UN, EU, and OECD, the study used content analysis to identify key ethical dimensions related to the use of generative AI in academia, such as accountability, human oversight, transparency, or inclusiveness. Empirical evidence was compiled from 30 leading universities ranked among the top 500 in the Shanghai Ranking list from May to July 2023, covering those institutions that already had publicly available responses to these dimensions in the form of policy documents or guidelines. The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings. This top-down requirement aligns with a bottom-up approach, allowing instructors flexibility in determining how they utilize generative AI especially large language models in their own courses. Regarding human oversight, the typical response identified by the study involves a blend of preventive measures (e.g., course assessment modifications) and soft, dialogue-based sanctioning procedures. The challenge of transparency induced the good practice of clear communication of AI use in course syllabi in the first university responses examined by this study.

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Introduction.

The competition in generative artificial intelligence (AI) ignited by the arrival of ChatGPT, the conversational platform based on a large language model (LLM) in late November 2022 (OpenAI, 2022 ) had a shocking effect even on those who are not involved in the industry (Rudolph et al. 2023 ). Within four months, on 22 March 2023, an open letter was signed by several hundred IT professionals, corporate stakeholders, and academics calling on all AI labs to immediately pause the training of AI systems more powerful than GPT-4 (i.e., those that may trick a human being into believing it is conversing with a peer rather than a machine) for at least six months (Future of Life Institute, 2023 ).

Despite these concerns, competition in generative AI and LLMs does not seem to lose momentum, forcing various social systems to overcome the existential distress they might feel about the changes and the uncertainty of what the future may bring (Roose, 2023 ). Organisations and individuals from different sectors of the economy and various industries are looking for adaptive strategies to accommodate the emerging new normal. This includes lawmakers, international organisations, employers, and employees, as well as academic and higher education institutions (Ray, 2023 ; Wach et al. 2023 ). This fierce competition generates gaps in real-time in everyday and academic life, the latter of which is also trying to make sense of the rapid technological advancement and its effects on university-level education (Perkins, 2023 ). Naturally, these gaps can only be filled, and relevant questions answered much slower by academia, making AI-related research topics timely.

This article aims to reduce the magnitude of these gaps and is intended to help leaders, administrators, teachers, and students better understand the ramifications of AI tools on higher education institutions. It will do so by providing a non-exhaustive snapshot of how various universities around the world responded to generative AI-induced ethical challenges in their everyday academic lives within six-eights months after the arrival of ChatGPT. Thus, the research had asked what expectations and guidelines the first policies introduced into existing academic structures to ensure the informed, transparent, responsible and ethical use of the new tools of generative AI (henceforth GAI) by students and teachers. Through reviewing and evaluating first responses and related difficulties the paper helps institutional decision-makers to create better policies to address AI issues specific to academia. The research reported here thus addressed actual answers to the question of what happened at the institutional (policy) level as opposed to what should happen with the use of AI in classrooms. Based on such a descriptive overview, one may contemplate normative recommendations and their realistic implementability.

Given the global nature of the study’s subject matter, the paper presents examples from various continents. Even though it was not yet a widespread practice to adopt separate, AI-related guidelines, the research focused on universities that had already done so quite early. Furthermore, as best practices most often accrue from the highest-ranking universities, the analysis only considered higher education institutions that were represented among the top 500 universities in the Shanghai Ranking list (containing 3041 Universities at the time), a commonly used source to rank academic excellence. Footnote 1 The main sources of this content analysis are internal documents (such as Codes of Ethics, Academic Regulations, Codes of Practice and Procedure, Guidelines for Students and Teachers or similar policy documents) from those institutions whose response to the GAI challenge was publicly accessible.

The investigation is organised around AI-related ethical dilemmas as concluded from relevant international documents, such as the instruments published by the UN, the EU, and the OECD (often considered soft law material). Through these sources, the study inductively identifies the primary aspects that these AI guidelines mention and can be connected to higher education. Thus it only contains concise references to the main ethical implications of the manifold pedagogical practices in which AI tools can be utilised in the classroom. The paper starts with a review of the challenges posed by AI technology to higher education with special focus on ethical dilemmas. Section 3 covers the research objective and the methodology followed. Section 4 presents the analysis of the selected international documents and establishes a list of key ethical principles relevant in HE contexts and in parallel presents the analysis of the examples distilled from the institutional policy documents and guidelines along that dimension. The paper closes with drawing key conclusions as well as listing limitations and ideas for future research.

Generative AI and higher education: Developments in the literature

General ai-related challenges in the classroom from a historical perspective.

Jacque Ellul fatalistically wrote already in 1954 that the “infusion of some more or less vague sentiment of human welfare” cannot fundamentally alter technology’s “rigorous autonomy”, bringing him to the conclusion that “technology never observes the distinction between moral and immoral use” (Ellul, 1964 , p. 97). Footnote 2 Jumping ahead nearly six decades, the above quote comes to the fore, among others, when evaluating the moral and ethical aspects of the services offered by specific software programs, like ChatGPT. While they might be trained to give ethical answers, these moral barriers can be circumvented by prompt injection (Blalock, 2022 ), or manipulated with tricks (Alberti, 2022 ), so generative AI platforms can hardly be held accountable for the inaccuracy of their responses Footnote 3 or how the physical user who inserted a prompt will make use of the output. Indeed, the AI chatbot is now considered to be a potentially disruptive technology in higher education practices (Farazouli et al. 2024 ).

Educators and educational institution leaders have from the beginning sought solutions on how “to use a variety of the strategies and technologies of the day to help their institutions adapt to dramatically changing social needs” (Miller, 2023 , p. 3). Education in the past had always had high hopes for applying the latest technological advances (Reiser, 2001 ; Howard and Mozejko, 2015 ), including the promise of providing personalised learning or using the latest tools to create and manage courses (Crompton and Burke, 2023 ).

The most basic (and original) educational settings include three components: the blackboard with chalk, the instructor, and textbooks as elementary “educational technologies” at any level (Reiser, 2001 ). Beyond these, one may talk about “educational media” which, once digital technology had entered the picture, have progressed from Computer Based Learning to Learning Management Systems to the use of the Internet, and lately to online shared learning environments with various stages in between including intelligent tutoring system, Dialogue-based Tutoring System, and Exploratory Learning Environment and Artificial Intelligence (Paek and Kim, 2021 ). And now the latest craze is about the generative form of AI often called conversational chatbot (Rudolph et al. 2023 ).

The above-mentioned promises appear to be no different in the case of using generative AI tools in education (Baskara, 2023a ; Mhlanga, 2023 ; Yan et al. 2023 ). The general claim is that GAI chatbots have transformative potential in HE (Mollick and Mollick, 2022 ; Ilieva et al. 2023 ). It is further alleged, that feedback mechanisms supposedly provided by GAI can be used to provide personalised guidance to students (Baskara, 2023b ). Some argue, that “AI education should be expanded and improved, especially by presenting realistic use cases and the real limitations of the technology, so that students are able to use AI confidently and responsibly in their professional future” (Almaraz-López et al. 2023 , p. 1). It is still debated whether the hype is justified, yet the question still remains, how to address the issues arising in the wake of the educational application of GAI tools (Ivanov, 2023 ; Memarian and Doleck, 2023 ).

Generative AI tools, such as their most-known representative, ChatGPT impact several areas of learning and teaching. From the point of view of students, chatbots may help with so-called Self-Regulated or Self-Determined Learning (Nicol and Macfarlane‐Dick, 2006 ; Baskara, 2023b ), where students either dialogue with chatbots or AI help with reviewing student work, even correcting it and giving feedback (Uchiyama et al. 2023 ). There are innovative ideas on how to use AI to support peer feedback (Bauer et al. 2023 ). Some consider that GAI can provide adaptive and personalised environments (Qadir, 2023 ) and may offer personalised tutoring (see, for example, Limo et al. ( 2023 ) on ChatGPT as a virtual tutor for personalized learning experiences). Furthermore, Yan et al. ( 2023 ) lists nine different categories of educational tasks that prior studies have attempted to automate using LLMs: Profiling and labelling (various educational or related content), Detection, Assessment and grading, Teaching support (in various educational and communication activities), Prediction, Knowledge representation, Feedback, Content generation (outline, questions, cases, etc.), Recommendation.

From the lecturers’ point of view, one of the most argued impacts is that assessment practices need to be revisited (Chaudhry et al. 2023 ; Gamage et al. 2023 ; Lim et al. 2023 ). For example, ChatGPT-written responses to exam questions may not be distinguished from student-written answers (Rudolph et al. 2023 ; Farazouli et al. 2024 ). Furthermore, essay-type works are facing special challenges (Sweeney, 2023 ). On the other hand, AI may be utilised to automate a range of educational tasks, such as test question generation, including open-ended questions, test correction, or even essay grading, feedback provision, analysing student feedback surveys, and so on (Mollick and Mollick, 2022 ; Rasul et al. 2023 ; Gimpel et al. 2023 ).

There is no convincing evidence, however, that either lecturers or dedicated tools are able to distinguish AI-written and student-written text with high enough accuracy that can be used to prove unethical behaviour in all cases (Akram, 2023 ). This led to concerns regarding the practicality and ethicality of such innovations (Yan et al. 2023 ). Indeed, the appearance of ChatGPT in higher education has reignited the (inconclusive) debate on the potential and risks associated with AI technologies (Ray, 2023 ; Rudolph et al. 2023 ).

When new technologies appear in or are considered for higher education, debates about their claimed advantages and potential drawbacks heat up as they are expected to disrupt traditional practices and require teachers to adapt to their potential benefits and drawbacks (as collected by Farrokhnia et al. 2023 ). One key area of such debates is the ethical issues raised by the growing accessibility of generative AI and discursive chatbots.

Key ethical challenges posed by AI in higher education

Yan et al. ( 2023 ), while investigating the practicality of AI in education in general, also consider ethicality in the context of educational technology and point out that related debates over the last decade (pre-ChatGPT, so to say), mostly focused on algorithmic ethics, i.e. concerns related to data mining and using AI in learning analytics. At the same time, the use of AI by teachers or, especially, by students has received less attention (or only under the scope or traditional human ethics). However, with the arrival of generative AI chatbots (such as ChatGPT), the number of publications about their use in higher education grew rapidly (Rasul et al. 2023 ; Yan et al. 2023 ).

The study by Chan ( 2023 ) offers a (general) policy framework for higher education institutions, although it focuses on one location and is based on the perceptions of students and teachers. While there are studies that collect factors to be considered for the ethical use of AI in HE, they appear to be restricted to ChatGPT (see, for example, Mhlanga ( 2023 )). Mhlanga ( 2023 ) presents six factors: respect for privacy, fairness, and non-discrimination, transparency in the use of ChatGPT, responsible use of AI (including clarifying its limitations), ChatGPT is not a substitute for human teachers, and accuracy of information. The framework by Chan ( 2023 ) is aimed at creating policies to teach students about GAI and considers three dimensions: pedagogical, governance, and operational. Within those dimensions, ten key areas identified covering ethical concerns such as academic integrity versus academic misconduct and related ethical dilemmas (e.g. cheating or plagiarism), data privacy, transparency, accountability and security, equity in access to AI technologies, critical AI literacy, over-reliance on AI technologies (not directly ethical), responsible use of AI (in general), competencies impeded by AI (such as leadership and teamwork). Baskara ( 2023b ), while also looking at ChatGPT only, considers the following likely danger areas: privacy, algorithmic bias issues, data security, and the potential negative impact of ChatGPT on learners’ autonomy and agency, The paper also questions the possible negative impact of GAI on social interaction and collaboration among learners. Although Yan et al. ( 2023 ) considers education in general (not HE in particular) during its review of 118 papers published since 2017 on the topic of AI ethics in education, its list of areas to look at is still relevant: transparency (of the models used), privacy (related to data collection and use by AI tools), equality (such as availability of AI tools in different languages), and beneficence (e.g. avoiding bias and avoiding biased and toxic knowledge from training data). While systematically reviewing recent publications about AI’s “morality footprint” in higher education, Memarian and Doleck ( 2023 ) consider the Fairness, Accountability, Transparency, and Ethics (FATE) approach as their framework of analyses. They note that “Ethics” appears to be the most used term as it serves as a general descriptor, while the other terms are typically only used in their descriptive sense, and their operationalisation is often lacking in related literature.

Regarding education-related data analytics, Khosravi et al. ( 2022 ) argue that educational technology that involves AI should consider accountability, explainability, fairness, interpretability and safety as key ethical concerns. Ferguson et al. ( 2016 ) also looked at learning analytics solutions using AI and warned of potential issues related to privacy, beneficence, and equality. M.A. Chaudhry et al. ( 2022 ) emphasise that enhancing the comprehension of stakeholders of a new educational AI system is the most important task, which requires making all information and decision processes available to those affected, therefore the key concern is related to transparency according to their arguments.

As such debates continue, it is difficult to identify an established definition of ethical AI in HE. It is clear, however, that the focus should not be on detecting academic misconduct (Rudolph et al. 2023 ). Instead, practical recommendations are required. This is especially true as even the latest studies focus mostly on issues related to assessment practices (Chan, 2023 ; Farazouli et al. 2024 ) and often limit their scope to ChatGPT (Cotton et al. 2024 ) (this specific tool still dominates discourses of LLMs despite the availability of many other solutions since its arrival). At the same time, the list of issues addressed appears to be arbitrary, and most publications do not look at actual practices on a global scale. Indeed, reviews of actual current practices of higher education institutions are rare, and this aspect is not yet the focus of recent HE AI ethics research reports.

As follows from the growing literature and the debate shaping up about the implications of using GAI tools in HE, there was a clear need for a systematic review of how first responses in actual academic policies and guidelines in practice have represented and addressed known ethical principles.

Research objective and methodology

In order to contribute to the debate on the impact of GAI on HE, this study aimed to review how leading institutions had reacted to the arrival of generative AI (such as ChatGPT) and what policies or institutional guidelines they have put in place shortly after. The research intended to understand whether key ethical principles were reflected in the first policy responses of HE institutions and, if yes, how they were handled.

As potential principles can diverge and could be numerous, as well as early guidelines may cover wide areas, the investigation is intended to be based on a few broad categories instead of trying to manage a large set of ideals and goals. To achieve this objective, the research was executed in three steps:

It was started with identifying and collecting general ethical ideals, which were then translated and structured for the context of higher education. A thorough content analysis was performed with the intention to put emphasis on positive values instead of simply focusing on issues or risks and their mitigation.

Given those positive ideals, this research collected actual examples of university policies and guidelines already available: this step was executed from May to July 2023 to find early responses addressing such norms and principles developed by leading HE institutions.

The documents identified were then analysed to understand how such norms and principles had been addressed by leading HE institutions.

As a result, this research managed to highlight and contrast differing practical views, and the findings raise awareness about the difficulties of creating relevant institutional policies. The research considered the ethics of using GAI and not expectations towards their development. The next two sections provide details of the two steps.

Establishing ethical principles for higher education

While the review of relevant ethical and HE literature (as presented above) was not fully conclusive, it highlighted the importance and need for some ideals specific to HE. Therefore, as a first step, this study sought to find highly respected sources of such ethical dimensions by executing a directed content analysis of relevant international regulatory and policy recommendations.

In order to establish what key values and ideas drive the formation of future AI regulations in general, Corrêa et al. ( 2023 ) investigated 200 publications discussing governance policies and ethical guidelines for using AI as proposed by various organisations (including national governments and institutions, civil society and academic organisations, private companies, as well as international bodies). The authors were also interested in whether there are common patterns or missing ideals and norms in this extensive set of proposals and recommendations. As the research was looking for key principles and normative attributes that could form a common ground for the comparison of HE policies, this vast set of documents was used to identify internationally recognised bodies that have potential real influence in this arena and decided to consider the guidelines and recommendations they have put forward for the ethical governance of AI. Therefore, for the purpose of this study, the following sources were selected (some organisations, such as the EU were represented by several bodies):

European Commission ( 2021 ): Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts (2021/0106 (COD)) . Footnote 4

European Parliament Committee on Culture and Education ( 2021 ): Report on artificial intelligence in education, culture and the audiovisual sector (2020/2017(INI)) . Footnote 5

High-Level Expert Group on Artificial Intelligence (EUHLEX) ( 2019 ): Ethics Guidelines for Trustworthy AI . Footnote 6

UNESCO ( 2022 ): Recommendation on the Ethics of Artificial Intelligence (SHS/BIO/PI/2021/1) . Footnote 7

OECD ( 2019 ): Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449) . Footnote 8

The ethical dilemmas established by these international documents (most of which is considered soft law material) were then used to inductively identify the primary aspects around which the investigation of educational AI principles may be organised.

Among the above documents, the EUHLEX material is the salient one as it contains a Glossary that defines and explains, among others, the two primary concepts that will be used in this paper: “artificial intelligence” and “ethics”. As this paper is, to a large extent, based on the deducted categorisation embedded in these international documents, it will follow suit in using the above terms as EUHLEX did, supporting it with the definitions contained in the other four referenced international documents. Consequently, artificial intelligence (AI) systems are referred to in this paper as software and hardware systems designed by humans that “act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal” (EUHLEX, 2019 ). With regards to ethics, the EUHLEX group defines this term, in general as an academic discipline which is a subfield of philosophy, dealing with questions like “What is a good action?”, “What is the value of a human life?”, “What is justice?”, or “What is the good life?”. It also mentions that academia distinguishes four major fields: (i) Meta-ethics, (ii) normative ethics, (iii) descriptive ethics, and (iv) applied ethics ” (EUHLEX, 2019 , p. 37). Within these, AI ethics belongs to the latter group of applied ethics that focuses on the practical issues raised by the design, development, implementation, and use of AI systems. By extension, the application of AI systems in higher education also falls under the domain of applied ethics.

The selection of sample universities

The collection of cases started with the AI guidelines compiled by the authors as members of the AI Committee at their university from May to July 2023. The AI Committee consisted of 12 members and investigated over 150 cases to gauge international best practices of GAI use in higher education when formulating a policy recommendation for their own university leadership. Given the global nature of the subject matter, examples from various continents were collected. From this initial pool authors narrowed the scope to the Top 500 higher education institutions of the Shanghai Ranking list for this study, as best practices most often accrue from the highest-ranking universities. Finally, only those institutions were included which, at the time of data collection, have indeed had publicly available policy documents or guidelines with clearly identifiable ethical considerations (such as relevant internal documents, Codes of Ethics, Academic Regulations, Codes of Practice and Procedure, or Guidelines for Students and Teachers). By the end of this selection process, 30 samples proved to be substantiated enough to be included in this study (presented in Table 1 ).

All documents were contextually analysed and annotated by both authors individually looking for references or mentions of ideas, actions or recommendations related to the ethical principles identified during the first step of the research. These comments were then compared and commonalities analysed regarding the nature and goal of the ethical recommendation.

Principles and practices of responsible use of AI in higher education

Ai-related ethical codes forming the base of this investigation.

A common feature of the selected AI ethics documents issued by international organisations is that they enumerate a set of ethical principles based on fundamental human values. The referenced international documents have different geographical- and policy scopes, yet they overlap in their categorisation of the ethical dimensions relevant to this research, even though they might use discrepant language to describe the same phenomenon (a factor we took into account when establishing key categories). For example, what EUHLEX dubs as “Human agency and oversight” is addressed by UNESCO under the section called “Human oversight and determination”, yet they essentially cover the same issues and recommended requirements. Among the many principles enshrined in these documents, the research focuses on those that can be directly linked to the everyday education practices of universities in relation to AI tools, omitting those that, within this context, are less situation-dependent and should normally form the overarching basis of the functioning of universities at all times, such as: respecting human rights and fundamental freedoms, refraining from all forms of discrimination, the right to privacy and data protection, or being aware of environmental concerns and responsibilities regarding sustainable development. As pointed out by Nikolinakos ( 2023 ), such principles and values provide essential guidance not only for development but also during the deployment and use of AI systems. Synthesising the common ethical codes in these instruments has led to the following cluster of ethical principles that are directly linked to AI-related higher education practices:

Accountability and responsibility;

Human agency and oversight;

Transparency and explainability

Inclusiveness and diversity.

The following subsections will give a comprehensive definition of these ethical areas and relate them to higher education expectations. Each subsection will first explain the corresponding ethical cluster, then present the specific university examples, concluding with a summary of the identified best practice under that particular cluster.

Accountability and responsibility

Definition in ethical codes and relevance.

The most fundamental requirements, appearing in almost all relevant documents, bring forward the necessity that mechanisms should be implemented to ensure responsibility and accountability for AI systems and their outcomes. These cover expectations both before and after their deployment, including development and use. They entail the basic requirements of auditability (i.e. the enablement of the assessment of algorithms), clear roles in the management of data and design processes (as a means for contributing to the trustworthiness of AI technology), the minimalisation and reporting of negative impacts (focusing on the possibility of identifying, assessing, documenting and reporting on the potential negative impacts of AI systems), as well as the ability of redress (understood as the capability to utilise mechanisms that offer legal and practical remedy when unjust adverse impact occurs) (EUHLEX, 2019 , pp. 19–20).

Additionally, Points 35–36 of the UNESCO recommendations remind us that it is imperative to “attribute ethical and legal responsibility for any stage of the life cycle of AI systems, as well as in cases of remedy related to AI systems, to physical persons or to existing legal entities. AI system can never replace ultimate human responsibility and accountability” (UNESCO, 2022 , p. 22).

The fulfilment of this fundamental principle is also expected from academic authors, as per the announcements of some of the largest publishing houses in the world. Accordingly, AI is not an author or co-author, Footnote 9 and AI-assisted technologies should not be cited as authors either, Footnote 10 given that AI-generated content cannot be considered capable of initiating an original piece of research without direction from human authors. The ethical guidelines of Wiley ( 2023 ) stated that ”[AI tools] also cannot be accountable for a published work or for research design, which is a generally held requirement of authorship, nor do they have legal standing or the ability to hold or assign copyright.” Footnote 11 This research angle carries over to teaching as well since students are also expected to produce outputs that are the results of their own work. Furthermore, they also often do their own research (such as literature search and review) in support of their projects, homework, thesis, and other forms of performance evaluation.

Accountability and responsibility in university first responses

The rapidly changing nature of the subject matter poses a significant challenge for scholars to assess the state of play of human responsibility. This is well exemplified by the reversal of hearts by some Australian universities (see Rudolph et al. ( 2023 ) quoting newspaper articles) who first disallowed the use of AI by students while doing assignments, just to reverse that decision a few months later and replace it by a requirement of disclosing the use of AI in homeworks. Similarly, Indian governments have been oscillating between a non-regulatory approach to foster an “innovation-friendly environment” for their universities in the summer of 2023 (Liu, 2023 ), only to roll back on this pledge a few months later (Dhaor, 2023 ).

Beyond this regulatory entropy, a fundamental principle enshrined in university codes of ethics across the globe is that students need to meet existing rules of scientific referencing and authorship. Footnote 12 In other words, they should refrain from any form of plagiarism in all their written work (including essays, theses, term papers, or in-class presentations). Submitting any work and assessments created by someone or something else (including AI-generated content) as if it was their own usually amounts to either a violation of scientific referencing, plagiarism or is considered to be a form of cheating (or a combination of these), depending on the terminology used by the respective higher education institution.

As a course description of Johns Hopkins puts it, “academic honesty is required in all work you submit to be graded …., you must solve all homework and programming assignments without the help of outside sources (e.g., GAI tools)” (Johns Hopkins University, 2023 ).

The Tokyo Institute of Technology applies a more flexible approach, as they “trust the independence of the students and expect the best use” of AI systems from them based on good sense and ethical standards. They add, however, that submitting reports that rely almost entirely on the output of GenAI is “highly improper, and its continued use is equivalent to one’s enslavement to the technology” (Tokyo Institute of Technology, 2023 ).

In the case of York University, the Senate’s Academic Standards, Curriculum, and Pedagogy Committee clarified in February 2023 that students are not authorised to use “text-, image-, code-, or video-generating AI tools when completing their academic work unless explicitly permitted by a specific instructor in a particular course” (York University Senate, 2023 ).

In the same time frame (6 February 2023), the University of Oxford stated in a guidance material for staff members that “the unauthorised use of AI tools in exams and other assessed work is a serious disciplinary offence” not permitted for students (University of Oxford, 2023b ).

Main message and best practice: honesty and mutual trust

In essence, students are not allowed to present AI-generated content as their own, Footnote 13 and they should have full responsibility and accountability for their own papers. Footnote 14 This is in line with the most ubiquitous principle enshrined in almost all university guidelines, irrespective of AI, that students are expected to complete their tasks based on their own knowledge and skills obtained throughout their education.

Given that the main challenge here is unauthorised use and overreliance on GAI platforms, the best practice answer is for students to adhere to academic honesty and integrity, scientific referencing standards, existing anti-plagiarism rules, and complete university assignments without fully relying on GAI tools, using, first and foremost, their own skills. The only exception is when instructed otherwise by their professors. By extension, preventing overuse and unauthorised use of AI assists students in avoiding undermining their own academic capacity-building efforts.

Human agency and oversight

AI systems have the potential to manipulate and influence human behaviour in ways that are not easily detectable. AI systems must, therefore, follow human-centric design principles and leave meaningful opportunities for human choice and intervention. Such systems should not be able to unjustifiably subordinate, coerce, deceive, manipulate, condition or herd humans (EUHLEX, 2019 , p. 16).

Human oversight thus refers to the capability for human intervention in every decision cycle of the AI system and the ability of users to make informed, autonomous decisions regarding AI systems. This encompasses the ability to choose not to use an AI system in a particular situation or to halt AI-related operations via a “stop” button or a comparable procedure in case the user detects anomalies, dysfunctions and unexpected performance from AI tools (European Commission, 2021 , Art. 14).

The sheer capability of active oversight and intervention vis-á-vis GAI systems is strongly linked to ethical responsibility and legal accountability. As Liao puts it, “the sufficient condition for human beings being rightsholders is that they have a physical basis for moral agency.” (Liao, 2020 , pp. 496–497). Wagner complemented this with the essential point that entity status for non-human actors would help to shield other parties from liability, i.e., primarily manufacturers and users (Wagner, 2018 ). This, in turn, would result in risk externalisation, which serves to minimise or relativise a person’s moral accountability and legal liability associated with wrongful or unethical acts.

Users, in our case, are primarily students who, at times, might be tempted to make use of AI tools in an unethical way, hoping to fulfil their university tasks faster and more efficiently than they could without these.

Human agency and oversight in university first responses

The crucial aspect of this ethical issue is the presence of a “stop” button or a similar regulatory procedure to streamline the operation of GAI tools. Existing university guidelines in this question point clearly in the direction of soft sanctions, if any, given the fact that there is a lack of evidence that AI detection platforms are effective and reliable tools to tell apart human work from AI-generated ones. Additionally, these tools raise some significant implications for privacy and data security issues, which is why university guidelines are particularly cautious when referring to these. Accordingly, the National Taiwan University, the University of Toronto, the University of Waterloo, the University of Miami, the National Autonomous University of Mexico, and Yale, among others, do not recommend the use of AI detection platforms in university assessments. The University of Zürich further added the moral perspective in a guidance note from 13 July 2023, that “forbidding the use of undetectable tools on unsupervised assignments or demanding some sort of honour code likely ends up punishing the honest students” (University of Zürich, 2023 ). Apart from unreliability, the University of Cape Town also drew attention in its guide for staff that AI detection tools may “disproportionately flag text written by non-first language speakers as AI-generated” (University of Cape Town, 2023 , p. 8).

Macquarie University took a slightly more ambiguous stance when they informed their staff that, while it is not “proof” for anything, an AI writing detection feature was launched within Turnitin as of 5 April 2023 (Hillier, 2023 ), claiming that the software has a 97% detection rate with a 1% false positive rate in the tests that they had conducted (Turnitin, 2023 ). Apart from these, Boston University is among the few examples that recommend employing AI detection tools, but only in a restricted manner to ”evaluate the degree to which AI tools have likely been employed” and not as a source for any punitive measures against students (University of Boston, 2023 ). Remarkably, they complement the above with suggestions for a merit-based scoring system, whereby instructors shall treat work by students who declare no use of AI tools as the baseline for grading. A lower baseline is suggested for students who declare the use of AI tools (depending on how extensive the usage was), and for the bottom of this spectrum, the university suggests imposing a significant penalty for low-energy or unreflective reuse of material generated by AI tools and assigning zero points for merely reproducing the output from AI platforms.

A discrepant approach was adopted at the University of Toronto. Here, if an instructor indicates that the use of AI tools is not permitted on an assessment, and a student is later found to have used such a tool nevertheless, then the instructor should consider meeting with the student as the first step of a dialogue-based process under the Code of Behaviour on Academic Matters (the same Code, which categorises the use of ChatGPT and other such tools as “unauthorised aid” or as “any other form of cheating” in case, an instructor specified that no outside assistance was permitted on an assignment) (University of Toronto, 2019 ).

More specifically, Imperial College London’s Guidance on the Use of Generative AI tools envisages the possibility of inviting a random selection of students to a so-called “authenticity interview” on their submitted assignments (Imperial College London, 2023b ). This entails requiring students to attend an oral examination of their submitted work to ensure its authenticity, which includes questions about the subject or how they approached their assignment.

As a rare exception, the University of Helsinki represents one of the more rigorous examples. The “Guidelines for the Use of AI in Teaching at the University of Helsinki” does not lay down any specific procedures for AI-related ethical offences. On the contrary, as para. 7 stipulates the unauthorised use of GAI in any course examination “constitutes cheating and will be treated in the same way as other cases of cheating” (University of Helsinki, 2023 ). Footnote 15

Those teachers who are reluctant to make AI tools a big part of their courses should rather aim to develop course assessment methods that can plausibly prevent the use of AI tools instead of attempting to filter these afterwards. Footnote 16 For example, the Humboldt-Universität zu Berlin instructs that, if possible, oral or practical examinations or written examinations performed on-site are recommended as alternatives to “classical” written home assignments (Humboldt-Universität zu Berlin, 2023a ).

Monash University also mentions some examples in this regard (Monash University, 2023a ), such as: asking students to create oral presentations, videos, and multimedia resources; asking them to incorporate more personal reflections tied to the concepts studied; implementing programmatic assessment that focuses on assessing broader attributes of students, using multiple methods rather than focusing on assessing individual kinds of knowledge or skills using a single assessment method (e.g., writing an essay).

Similarly, the University of Toronto suggest instructors to: ask students to respond to a specific reading that is very new and thus has a limited online footprint; assign group work to be completed in class, with each member contributing; or ask students to create a first draft of an assignment by hand, which could be complemented by a call to explain or justify certain elements of their work (University of Toronto, 2023 ).

Main message and best practice: Avoiding overreaction

In summary, the best practice that can be identified under this ethical dilemma is to secure human oversight through a blend of preventive measures (e.g. a shift in assessment methods) and soft sanctions. Given that AI detectors are unreliable and can cause a series of data privacy issues, the sanctioning of unauthorised AI use should happen on a “soft basis”, as part of a dialogue with the student concerned. Additionally, universities need to be aware and pay due attention to potentially unwanted rebound effects of bona fide measures, such as the merit-based scoring system of the University of Boston. In that case, using different scoring baselines based on the self-declared use of AI could, in practice, generate incentives for not declaring any use of AI at all, thereby producing counter-effective results.

While explainability refers to providing intelligible insight into the functioning of AI tools with a special focus on the interplay between the user’s input and the received output, transparency alludes to the requirement of providing unambiguous communication in the framework of system use.

As the European Commission’s Regulation proposal ( 2021 ) puts it under subchapter 5.2.4., transparency obligations should apply for systems that „(i) interact with humans, (ii) are used to detect emotions or determine association with (social) categories based on biometric data, or (iii) generate or manipulate content (‘deep fakes’). When persons interact with an AI system or their emotions or characteristics are recognised through automated means, people must be informed of that circumstance. If an AI system is used to generate or manipulate image, audio or video content that appreciably resembles authentic content, there should be an obligation to disclose that the content is generated through automated means, subject to exceptions for legitimate purposes (law enforcement, freedom of expression). This allows persons to make informed choices or step back from a given situation.”

People (in our case, university students and teachers) should, therefore, be fully informed when a decision is influenced by or relies on AI algorithms. In such instances, individuals should be able to ask for further explanation from the decision-maker using AI (e.g., a university body). Furthermore, individuals should be afforded the choice to present their case to a dedicated representative of the organisation in question who should have the power to reviset the decision and make corrections if necessary (UNESCO, 2022 , p. 22). Therefore, in the context of courses and other related education events, teachers should be clear about their utilisation of AI during the preparation of the material. Furthermore, instructors must unambiguously clarify ethical AI use in the classroom. Clear communication is essential about whether students have permission to utilise AI tools during assignments and how to report actual use.

As both UN and EU sources point out, raising awareness about and promoting basic AI literacy should be fostered as a means to empower people and reduce the digital divides and digital access inequalities resulting from the broad adoption of AI systems (EUHLEX, 2019 , p. 23; UNESCO, 2022 , p. 34).

Transparency and explainability in university first responses

The implementation of this principle seems to revolve around the challenge of decentralisation of university work, including the respect for teachers’ autonomy.

Teachers’ autonomy entails that teachers can decide if and to what extent they will allow their students to use AI platforms as part of their respective courses. This, however, comes with the essential corollary, that they must clearly communicate their decision to both students and university management in the course syllabus. To support transparency in this respect, many universities decided to establish 3-level- or 4-level admissibility frameworks (and even those who did not establish such multi-level systems, e.g., the University of Toronto, urge instructors to explicitly indicate in the course syllabus the expected use of AI) (University of Toronto, 2023 ).

The University of Auckland is among the universities that apply a fully laissez passer laissez-faire approach in this respect, meaning that there is a lack of centralised guidance or recommendations on this subject. They rather confer all practical decision-making of GAI use on course directors, adding that it is ultimately the student’s responsibility to correctly acknowledge the use of Gen-AI software (University of Auckland, 2023 ). Similarly, the University of Helsinki gives as much manoeuvring space to their staff as to allow them to change the course of action during the semester. As para 1 of their earlier quoted Guidelines stipulates, teachers are responsible for deciding how GAI can be used on a given course and are free to fully prohibit their use if they think it impedes the achievement of the learning objectives.

Colorado State University, for example, provides its teachers with 3 types of syllabus statement options (Colorado State University, 2023 ): (a) the prohibitive statement: whereby any work created, or inspired by AI agents is considered plagiarism and will not be tolerated; (b) the use-with-permission statement: whereby generative AI can be used but only as an exception and in line with the teachers further instruction, and (c) the abdication statement: where the teacher acknowledges that the course grade will also be a reflection of the students ability to harness AI technologies as part of their preparation for their future in a workforce that will increasingly require AI-literacy.

Macquarie University applies a similar system and provides it’s professors with an Assessment Checklist in which AI use can be either “Not permitted” or “Some use permitted” (meaning that the scope of use is limited while the majority of the work should be written or made by the student.), or “Full use permitted (with attribution)”, alluding to the adaptive use of AI tools, where the generated content is edited, mixed, adapted and integrated into the student’s final submission – with attribution of the source (Macquarie University, 2023 ).

The same approach is used at Monash University where generative AI tools can be: (a) used for all assessments in a specific unit; (b) cannot be used for any assessments; (c) some AI tools may be used selectively (Monash University, 2023b ).

The University of Cape Town (UCT) applies a 3-tier system not just in terms of the overall approach to the use or banning of GAI, but also with regard to specific assessment approaches recommended to teachers. As far as the former is concerned, they differentiate between the strategies of: (a) Avoiding (reverting to in-person assessment, where the use of AI isn’t possible); (b) Outrunning (devising an assessment that AI cannot produce); and (c) Embracing (discussing the appropriate use of AI with students and its ethical use to create the circumstances for authentic assessment outputs). The assessment possibilities, in turn, are categorised into easy, medium, and hard levels. Easy tasks include, e.g., generic short written assignments. Medium level might include examples such as personalised or context-based assessments (e.g. asking students to write to a particular audience whose knowledge and values must be considered or asking questions that would require them to give a response that draws from concepts that were learnt in class, in a lab, field trip…etc). In contrast, hard assessments include projects involving real-world applications, synchronous oral assessments, or panel assessments (University of Cape Town, 2023 ).

4-tier-systems are analogues. The only difference is that they break down the “middle ground”. Accordingly, the Chinese University of Hong Kong clarifies that Approach 1 (by default) means the prohibition of all use of AI tools; Approach 2 entails using AI tools only with prior permission; Approach 3 means using AI tools only with explicit acknowledgement; and Approach 4 is reserved for courses in which the use of AI tools is freely permitted with no acknowledgement needed (Chinese University of Hong Kong, 2023 ).

Similarly, the University of Delaware provides course syllabus statement examples for teachers including: (1) Prohibiting all use of AI tools; (2) Allowing their use only with prior permission; (3) Allow their use only with explicit acknowledgement; (4) Freely allow their use (University of Delaware, 2023 ).

The Technical University of Berlin also proposes a 4-tier system but uses a very different logic based on the practical knowledge one can obtain by using GAI. Accordingly, they divide AI tools as used to: (a) acquire professional competence; (b) learn to write scientifically; (c) be able to assess AI tools and compare them with scientific methods; d) professional use of AI tools in scientific work. Their corresponding guideline even quotes Art. 5 of the German Constitution referencing the freedom of teaching ( Freiheit der Lehre ), entailing that teachers should have the ability to decide for themselves which teaching aids they allow or prohibit. Footnote 17

This detailed approach, however, is rather the exception. According to the compilation on 6 May 2023 by Solis ( 2023 ), among the 100 largest German universities, 2% applied a general prohibition on the use of ChatGPT, 23% granted partial permission, 12% generally permitted its use, while 63% of the universities had none or only vague guidelines in this respect.

Main message and best practice: raising awareness

Overall, the best practice answer to the dilemma of transparency is the internal decentralisation of university work and the application of a “bottom-up” approach that respects the autonomy of university professors. Notwithstanding the potential existence of regulatory frameworks that set out binding rules for all citizens of an HE institution, this means providing university instructors with proper manoeuvring space to decide on their own how they would like to make AI use permissible in their courses, insofar as they communicate their decision openly.

Inclusiveness and diversity

Para. 34 of the Report by the European Parliament Committee on Culture and Education ( 2021 ) highlights that inclusive education can only be reached with the proactive presence of teachers and stresses that “AI technologies cannot be used to the detriment or at the expense of in-person education, as teachers must not be replaced by any AI or AI-related technologies”. Additionally, para. 20 of the same document highlights the need to create diverse teams of developers and engineers to work alongside the main actors in the educational, cultural, and audiovisual sectors in order to prevent gender or social bias from being inadvertently included in AI algorithms, systems, and applications.

This approach also underlines the need to consider the variety of different theories through which AI has been developed as a precursor to ensuring the application of the principle of diversity (UNESCO, 2022 , pp. 33–35), and it also recognises that a nuanced answer to AI-related challenges is only possible if affected stakeholders have an equal say in regulatory and design processes. An idea closely linked to the principle of fairness and the pledge to leave no one behind who might be affected by the outcome of using AI systems (EUHLEX, 2019 , pp. 18–19).

Therefore, in the context of higher education, the principle of inclusiveness aims to ensure that an institution provides the same opportunities to access the benefits of AI technologies for all its students, irrespective of their background, while also considering the particular needs of various vulnerable groups potentially marginalised based on age, gender, culture, religion, language, or disabilities. Footnote 18 Inclusiveness also alludes to stakeholder participation in internal university dialogues on the use and impact of AI systems (including students, teachers, administration and leadership) as well as in the constant evaluation of how these systems evolve. On a broader scale, it implies communication with policymakers on how higher education should accommodate itself to this rapidly changing environment (EUHLEX, 2019 , p. 23; UNESCO, 2022 , p. 35).

Inclusiveness and diversity in university first responses

Universities appear to be aware of the potential disadvantages for students who are either unfamiliar with GAI or who choose not to use it or use it in an unethical manner. As a result, many universities thought that the best way to foster inclusive GAI use was to offer specific examples of how teachers could constructively incorporate these tools into their courses.

The University of Waterloo, for example, recommends various methods that instructors can apply on sight, with the same set of tools for all students during their courses, which in itself mitigates the effects of any discrepancies in varying student backgrounds (University of Waterloo, 2023 ): (a) Give students a prompt during class, and the resulting text and ask them to critique and improve it using track changes; (b) Create two distinct texts and have students explain the flaws of each or combine them in some way using track changes; (c) Test code and documentation accuracy with a peer; or (d) Use ChatGPT to provide a preliminary summary of an issue as a jumping-off point for further research and discussion.

The University of Pittsburgh ( 2023 ) and Monash added similar recommendations to their AI guidelines (Monash University, 2023c ).

The University of Cambridge mentions under its AI-deas initiative a series of projects aimed to develop new AI methods to understand and address sensory, neural or linguistic challenges such as hearing loss, brain injury or language barriers to support people who find communicating a daily challenge in order to improve equity and inclusion. As they put it, “with AI we can assess and diagnose common language and communication conditions at scale, and develop technologies such as intelligent hearing aids, real-time machine translation, or other language aids to support affected individuals at home, work or school.” (University of Cambridge, 2023 ).

The homepage of the Technical University of Berlin (Technische Universität Berlin) displays ample and diverse materials, including videos Footnote 19 and other documents, as a source of inspiration for teachers on how to provide an equitable share of AI knowledge for their students (Glathe et al. 2023 ). More progressively, the university’s Institute of Psychology offers a learning modul called “Inclusive Digitalisation”, available for students enrolled in various degree programmes to understand inclusion and exclusion mechanisms in digitalisation. This modul touches upon topics such as barrier-free software design, mechanisms and reasons for digitalised discrimination or biases in corporate practices (their homepage specifically alludes to the fact that input and output devices, such as VR glasses, have exclusively undergone testing with male test subjects and that the development of digital products and services is predominantly carried out by men. The practical ramifications of such a bias result in input and output devices that are less appropriate for women and children) (Technische Universität Berlin, 2023 ).

Columbia recommends the practice of “scaffolding”, which is the process of breaking down a larger assignment into subtasks (Columbia University, 2023 ). In their understanding, this method facilitates regular check-ins and enables students to receive timely feedback throughout the learning process. Simultaneously, the implementation of scaffolding helps instructors become more familiar with students and their work as the semester progresses, allowing them to take additional steps in the case of students who might need more attention due to their vulnerable backgrounds or disabilities to complete the same tasks.

The Humboldt-Universität zu Berlin, in its Recommendations, clearly links the permission of GAI use with the requirement of equal accessibility. They remind that if examiners require students to use AI for an examination, “students must be provided with access to these technologies free of charge and in compliance with data protection regulations” (Humboldt-Universität zu Berlin, 2023b ).

Concurringly, the University of Cape Town also links inclusivity to accessibility. As they put it, “there is a risk that those with poorer access to connectivity, devices, data and literacies will get unequal access to the opportunities being provided by AI”, leading to the conclusion that the planning of the admissible use of GAI on campus should be cognizant of access inequalities (University of Cape Town, 2023 ). They also draw their staff’s attention to a UNESCO guide material containing useful methods to incorporate ChatGPT into the course, including methods such as the “Socratic opponent” (AI acts as an opponent to develop an argument), the “study buddy” (AI helps the student reflect on learning material) or the “dynamic assessor” (AI provides educators with a profile of each student’s current knowledge based on their interactions with ChatGPT) (UNESCO International Institute for Higher Education in Latin America and the Caribbean, 2023 ).

Finally, the National Autonomous University of Mexico’s Recommendations suggest using GAI tools, among others, for the purposes of community development. They suggest that such community-building activities, whether online or in live groups, kill two birds with one stone. On the one hand, they assist individuals in keeping their knowledge up to date with a topic that is constantly evolving, while it offers people from various backgrounds the opportunity to become part of communities in the process where they can share their experiences and build new relations (National Autonomous University of Mexico, 2023 ).

Main message and best practice: Proactive central support and the pledge to leave no one behind

To conclude, AI-related inclusivity for students is best fostered if the university does not leave its professors solely to their own resources to come up with diverging initiatives. The best practice example for this dilemma thus lies in a proactive approach that results in the elaboration of concrete teaching materials (e.g., subscriptions to AI tools to ensure equal accessibility for all students, templates, video tutorials, open-access answers to FAQs…etc.), specific ideas, recommendations and to support specialised programmes and collaborations with an inclusion-generating edge. With centrally offered resources and tools institutions seem to be able to ensure accessability irrespective of students’ background and financial abilities.

Discussion of the First Responses

While artificial intelligence and even its generative form has been around for a while, the arrival of application-ready LLMs – most notably ChatGPT has changed the game when it comes to grammatically correct large-scale and content-specific text generation. This has invoked an immediate reaction from the higher education community as the question arose as to how it may affect various forms of student performance evaluation (such as essay and thesis writing) (Chaudhry et al. 2023 ; Yu, 2023 ; Farazouli et al. 2024 ).

Often the very first reaction (a few months after the announcement of the availability of ChatGPT) was a ban on these tools and a potential return to hand-written evaluation and oral exams. In the institutions investigated under this research, notable examples may be most Australian universities (such as Monash) or even Oxford. On the other hand, even leading institutions have immediately embraced this new tool as a great potential helper of lecturers – the top name here being Harvard. Very early responses thus ranged widely – and have changed fast over the first six-eight months “post-ChatGPT”.

Over time responses from the institutions investigated started to put out clear guidelines and even created dedicated policies or modified existing ones to ensure a framework of acceptable use. The inspiration leading these early regulatory efforts was influenced by the international ethics documents reviewed in this paper. Institutions were aware of and relied on those guidelines. The main goal of this research was to shed light on the questions of how much and in what ways they took them on board regarding first responses. Most first reactions were based on “traditional” AI ethics and understanding of AI before LLMs and the generative revolution. First responses by institutions were not based on scientific literature or arguments from journal publications. Instead, as our results demonstrated it was based on publicly available ethical norms and guidelines published by well-known international organizations and professional bodies.

Conclusions, limitations and future research

Ethical dilemmas discussed in this paper were based on the conceptualisation embedded in relevant documents of various international fora. Each ethical dimension, while multifaceted in itself, forms a complex set of challenges that are inextricably intertwined with one another. Browsing university materials, the overall impression is that Universities primarily aim to explore and harness the potential benefits of generative AI but not with an uncritical mindset. They are focusing on the opportunities while simultaneously trying to address the emerging challenges in the field.

Accordingly, the main ethical imperative is that students must complete university assignments based on the knowledge and skills they acquired during their university education unless their instructors determine otherwise. Moral and legal responsibility in this regard always rests with human individuals. AI agents possess neither the legal standing nor the physical basis for moral agency, which makes them incapable of assuming such responsibilities. This “top-down” requirement is most often complemented by the “bottom-up” approach of providing instructors with proper maneuvering space to decide how they would like to make AI use permissible in their courses.

Good practice in human oversight could thus be achieved through a combination of preventive measures and soft, dialogue-based procedures. This latter category includes the simple act of teachers providing clear, written communications in their syllabi and engaging in a dialogue with their students to provide unambiguous and transparent instructions on the use of generative AI tools within their courses. Additionally, to prevent the unauthorised use of AI tools, changing course assessment methods by default is more effective than engaging in post-assessment review due to the unreliability of AI detection tools.

Among the many ethical dilemmas that generative AI tools pose to social systems, this paper focused on those pertaining to the pedagogical aspects of higher education. Due to this limitation, related fields, such as university research, were excluded from the scope of the analysis. However, research-related activities are certainly ripe for scientific scrutiny along the lines indicated in this study. Furthermore, only a limited set of institutions could be investigated, those who were the ”first respondents” to the set of issues covered by this study. Hereby, this paper hopes to inspire further research on the impact of AI tools on higher education. Such research could cover more institutions, but it would also be interesting to revisit the same institutions again to see how their stance and approach might have changed over time considering how fast this technology evolves and how much we learn about its capabilities and shortcomings.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. All documents referenced in this study are publicly available on the corresponding websites provided in the Bibliography or in the footnotes. No code has been developed as part of this research.

For the methodology behind the Shanghai Rankings see: https://www.shanghairanking.com/methodology/arwu/2022 . Accessed: 14 November 2023.

While the original French version was published in 1954, the first English translation is dated 1964.

As the evaluation by Bang et al. ( 2023 ) found, ChatGPT is only 63.41% accurate on average in ten different reasoning categories under logical reasoning, non-textual reasoning, and common-sense reasoning, making it an unreliable reasoner.

Source: https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence . Accessed: 14 November 2023.

Source https://www.europarl.europa.eu/doceo/document/A-9-2021-0127_EN.html . Accessed: 14 November 2023.

Source: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai . Accessed: 14 November 2023.

Source: https://unesdoc.unesco.org/ark:/48223/pf0000381137 . Accessed: 14 November 2023.

Source: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449#mainText . Accessed: 14 November 2023.

The editors-in-chief of Nature and Science stated that ChatGPT does not meet the standard for authorship: „ An attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs…. We would not allow AI to be listed as an author on a paper we published, and use of AI-generated text without proper citation could be considered plagiarism,” (Stokel-Walker, 2023 ). See also (Nature, 2023 ).

While there was an initial mistake that credited ChatGPT as an author of an academic paper, Elsevier issued a Corrigendum on the subject in February 2023 (O’Connor, 2023 ). Elsevier then clarified in its “Use of AI and AI-assisted technologies in writing for Elsevier” announcement, issued in March 2023, that “Authors should not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author”. See https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier . Accessed 23 Nov 2023.

The ethical guidelines of Wiley was updated on 28 February 2023 to clarify the publishing house’s stance on AI-generated content.

See e.g.: Section 2.4 of Princeton University’s Academic Regulations (Princeton University, 2023 ); the Code of Practice and Procedure regarding Misconduct in Research of the University of Oxford (University of Oxford, 2023a ); Section 2.1.1 of the Senate Guidelines on Academic Honesty of York University, enumerating cases of cheating (York University, 2011 ); Imperial College London’s Academic Misconduct Policy and Procedures document (Imperial College London, 2023a ); the Guidelines for seminar and term papers of the University of Vienna (Universität Wien, 2016 ); Para 4. § (1) - (4) of the Anti-plagiarism Regulation of the Corvinus University of Budapest (Corvinus University of Budapest, 2018 ), to name a few.

15 Art. 2 (c)(v) of the early Terms of Use of OpenAI Products (including ChatGPT) dated 14 March 2023 clarified the restrictions of the use of their products. Accordingly, users may not represent the output from their services as human-generated when it was not ( https://openai.com/policies/mar-2023-terms/ . Accessed 14 Nov 2023). Higher education institutions tend to follow suit with this policy. For example, the List of Student Responsibilities enumerated under the “Policies and Regulations” of the Harvard Summer School from 2023 reminds students that their “academic integrity policy forbids students to represent work as their own that they did not write, code, or create” (Harvard University, 2023 ).

A similar view was communicated by Taylor & Francis in a press release issued on 17 February 2023, in which they clarified that: “Authors are accountable for the originality, validity and integrity of the content of their submissions. In choosing to use AI tools, authors are expected to do so responsibly and in accordance with our editorial policies on authorship and principles of publishing ethics” (Taylor and Francis, 2023 ).

This is one of the rare examples where the guideline was adopted by the university’s senior management, in this case, the Academic Affairs Council.

It should be noted that abundant sources recommend harnessing AI tools’ opportunities to improve education instead of attempting to ban them. Heaven, among others, advocated on the pages of the MIT Technology Review the use of advanced chatbots such as ChatGPT as these could be used as “powerful classroom aids that make lessons more interactive, teach students media literacy, generate personalised lesson plans, save teachers time on admin” (Heaven, 2023 ).

This university based its policies on the recommendations of the German Association for University Didactics (Deutsche Gesellschaft für Hochschuldidaktik). Consequently, they draw their students’ attention to the corresponding material, see: (Glathe et al. 2023 ).

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Dabis, A., Csáki, C. AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanit Soc Sci Commun 11 , 1006 (2024). https://doi.org/10.1057/s41599-024-03526-z

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Received : 21 February 2024

Accepted : 29 July 2024

Published : 06 August 2024

DOI : https://doi.org/10.1057/s41599-024-03526-z

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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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