Plagiarism

"Plagiarism" is the word for using another person's literal expressions (words, images, etc.) or representing their ideas or concepts as your own, or in place of your own work.

Any amount of misrepresented work, large or small, passed off as your own, is plagiarism as a form of academic dishonesty.

People will be relying on you. We want you to get used to being able to be trusted when you say things, and to deserve that trust because the things you say are both yours, and solidly grounded in a truth based on doing justice to others in community.

In academic work you are expected to use your own words, and represent your own thoughts and ideas and concepts, which you have developed in the process of engaging with the work of many other people.

You are therefore expected to keep track of all of the other work you have read, and the expressions and ideas you have found there, and to be able to say clearly whose they are, and where they came from.

You are expected to be able to support your own words, your own thoughts and ideas and concepts, through exact references to all of that other work where it agrees with you, and to be able to argue with it in detail where you think something different.

We want to hear from you, and we value your work, done in respectful community with your fellow scholars. Any other source must be cited explicitly, because they are also artists and authors we care about, part of our community.

Every author you cite well, becomes part of the circle. Every author you plagiarize, is excluded from a place in community they have earned. Do justice to their work, and you do justice to your own.

The JKM Library is here to help with your research, and McCormick and LSTC also offer writing help through their own writing centers, including help with English, editing, and style guides.

 

Citation Guides  |  School Policies

Plagiarism and "AI"

 

Citation Guides

Accurately crediting and regularly citing your sources is an essential aspect of avoiding plagiarism.

It is important to know what information you need to quote, to paraphrase, and to cite, and how to do so properly for your sources and project.

The JKM Library is here to support you. We provide access to the following style guides, which will help you with a variety of sources and projects:

If you are not sure which to use, or how to use them for your project, please reach out first to your professor, advisor, or the supervisor of your project for specific advice.

You can also email JKM staff at ihaveaquestion@jkmlibrary.org for more general information.

 

School Policies

McCormick and LSTC each have their own descriptions of what constitutes academic integrity and plagiarism, and policies for dealing with it:

Other schools and programs have their own helpful descriptions and advice for dealing with plagiarism:

 

Plagiarism and "AI"

Large Language Model (LLM) "AI," also called "generative AI," is very popular today, and presents some serious problems from the standpoint of academic integrity.

These "AI" systems are capable of taking a brief prompt, and generating images or text on the basis of their training data. Those images will look mostly like other images you see in artworks and on the internet. That text will sound mostly like other text you might read in books or on the internet.

That may sound tempting to you! Every author struggles to find the right words, after all. Plagiarism has always been tempting, for this exact reason—and "AI" systems make it easier than ever, while obscuring the real sources.

We expect you to resist that temptation, because we expect you to learn, and you can only learn by doing. When you give in, you do not develop your own right words. When you give in, you also do not respect the right words of others.

Remember: the point of academic work is that we expect your words, your work. We care about you. We expect you to make mistakes, and learn, and grow. If you're worried about quality, just keep writing. Work with your teachers and your peers. You will find your voice. Your words and your work will only get better through practice!

 

"AI" companies engage in systematic copyright violation and plagiarism

It is important to recognize that the output from LLM "AI" systems always comes from somewhere, and not from the "AI" itself.

The "AI" output text and images are derived from a set of sources that these systems are designed to obscure, so that the companies behind them can receive the credit.

Called "training data," the sources used by LLM "AI" systems are in fact copyrighted works taken from existing authors and artists. "AI" output looks like their work, because it is taken from their work.

This has been done without author or artist consent, without compensation for their hard work, and without citation when that work is reproduced, in whole or in part, by companies intent on generating profit.

This means that "generative" or LLM "AI" is built on copyright violation, on a massive scale, and designed to plagiarize the works it is based on. Everything it does is built on this theft, as is everything done using it.

This is the root of the problem, when it comes to whether or how you use "generative" or LLM "AI" in your academic work.

These companies want you to participate in that theft, and to imagine that doing so helps—rather than harms—your own work, your own development, your own creativity.

 

"AI" generated words are not your words

When it comes to LLM "AI" as a source of text, you should treat its output like any other source of text you did not write. No matter how much work you put into designing your prompt, you did not write the "AI" output.

The words the "AI" produces are the words of others, however modified or remixed, provided without credit. They are not your words; they did not come from you.

You are therefore not responsible for what the output text says, though you are responsible for having used the system to produce it. And you are responsible for how you use that text, like any other words not your own.

If you receive LLM "AI" output text, and present that text as your own, you are engaging in plagiarism as a form of academic dishonesty.

You are likely to be engaging in plagiarism from real authors, and in addition to academic penalties, this may create the risk of lawsuit for copyright violation if you publish such material.

Modifying that text cannot solve the fact that it is plagiarism; it can only seek to hide it, just as the "AI" system does to its uncredited sources.

 

Citation does not solve the problem of "AI" text

Unfortunately, the problem of plagiarism when using LLM "AI" output cannot be solved by proper citation.

You can and should admit where you got these words, as they are not yours. However, LLM "AI" cannot give you a text for which citation is in any way meaningful.

The point of citation is to demonstrate the authorship and origin of the material that you are citing. This enables your reader to check your work against those sources, which still exist outside of your own work.

Even if you did cite an "AI" system, their output is inconsistent and highly variable. Even if it included your prompt, your citation would not guarantee your reader access to the same text you generated, much less any larger context for it. Such a citation gives no context information beyond the fact of plagiarism.

Nor will citing the "AI" system accurately source the words and ideas of its training data. While the output from LLM "AI" systems always comes from somewhere, and not from the "AI" itself, that material has already been plagiarized by the "AI" system, presented in an obscured form and without citation in order to hide credit due the texts and authors used.

Citation of a plagiarized work builds on its injustice against the uncredited authors.

 

"AI" generated citations are fake

Nor can you trust the solution the "AI" system might present to this problem.

LLM "AI" systems routinely invent false "citations" to work which simply does not exist, to justify answers equally made up—answers which often bear no resemblance to reality, factually incorrect in subtle as well as obvious ways.

Even if the "AI" system manages to generate the name of a work that exists, by an author that exists, it does so based purely on linguistic probability, not fact. This is not a citation, because you have no guarantee that the work named is in any way related to the text generated, or any claims it makes.

LLM "AI" systems are being used to replace the work of others you should be reading and citing. Even if you try to evaluate their output, that will always take a large amount of effort, result overwhelmingly in disproof, and you will still have to do the work that these systems leave undone.

The only solution to this problem is for you to do your own sourcework from the beginning, with genuinely authored books and articles that exist for both you and your audience; to take notes and keep track of whose words and ideas came from where, when; and to develop your own ideas and arguments in honest conversation with them.

 

Your library is here to help!

You have libraries at your disposal, including the JKM Library and all of our partner libraries, which are full of reliable texts, which will still be there when you or your readers come back to them.

These are texts you must cite properly, but they are also texts of which you may be critical. They may also be wrong, but their authors were trying, in the best case, to learn the truth and to be right. And you may even help prove some of them wrong, but that will be a meaningful disagreement, and maybe even a very important one! 

JKM Library staff, alongside the faculty and staff of McCormick and LSTC, will gladly help you through your process of becoming a capable and talented scholar using reliable resources.

We look forward to being able to share with you in the pride of your legitimate accomplishments. Do not cheat yourself, and us, of that joy!

 

 

Postscript: Why is this the case?

You may wonder: if all this is so, how does "generative" or LLM "AI" actually work? Is there anything I can do to make it work better?

 

"AI" systems analyze and reproduce statistical patterns, not matters of fact

No matter how many facts may be in its training data, LLM "AI" systems are not programmed to understand that questions have factual answers, found by direct reference to external data. They are not designed to reproduce the data itself, for that matter.

Put simply: LLM "AI" systems are designed to detect and reproduce patterns, not factual data. Their core concern is the statistical likelihood that similar-looking material—language or imagery—appears in similar patterns elsewhere. They are designed to use the examples included in their training data to respond to your prompt by manufacturing output according to its patterns.

Furthermore, LLM "AI" systems do not have any sense of the meaning of the data they have been trained on. Textual training data has been processed for its linguistic patterns. These systems are programmed to notice what patterns appear most often in that language, and then to manufacture language that looks similar to those patterns, on the assumption that the most common patterns are correct, and that matching those patterns will satisfy your interest.

The "AI" system is most likely to give a response that is only linguistically, not factually, similar to the desired real-world data by some standard. It is likely to contain a variety of errors, from the subtle to the absurd. And it may even bear no resemblance to reality at all.

If meaning has been imposed, it has been done by human workers alongside the "AI" system, whether or not they can be trusted to understand the data themselves. (Software companies are not heavily staffed with subject-area experts.) This most often takes the form of a system of "weights" assigned to adjust the normal output of the LLM "AI" system, and those weights are subject to a high degree of bias. They can help limit the degree to which the system produces obvious nonsense, but they can and do also advance a variety of political agendas, and create their own degree of nonsense output by pitting ideology against factual data.

There is therefore some chance that the output of an LLM "AI" system might be correct—if the most common data appearing in the most common pattern of response in its training data contains the correct answer, and if artificial weight has not been imposed on some other answer. However, there remains no way to guarantee that the system will reproduce desired factual data, instead of manufacturing some other language. If it does reproduce that data, it will do so without citation, leaving you ignorant of information vital to your academic work. And if it does provide what appears to be a citation, that "citation" itself has been generated in the same way as all other "AI" output, and is likely to be fictional.

LLM "AI" systems operate in this way whether or not the generated output answers your question in any way, let alone correctly. To the extent that you may have complaints, these systems rely on your objections to adjust their output until it is satisfactory to you. This makes them radically unsuitable for any research work, where you are trying to learn things you do not yet know. Worse, just as we hear of "AI" chatbots reinforcing people's psychological pathologies, they will tend to reinforce your research pathologies, instead of helping you correct them.

The same is true of summarizing text, another common application for which LLM "AI" is not suited. Once again, it does not understand the meaning of the text, only its patterns. Its summary is likely to be linguistically, and not necessarily factually, "similar" to the text. However, that summary may bear no resemblance to the text on key points, and worse, it is likely to introduce language that does not exist in the text, but does exist in the LLM "AI" training data.

 

"AI" systems add extra time and work over and above actual research

It is possible for companies to spend money and engineering time to force any LLM "AI" system to operate in ways that reduce (but cannot eliminate) its inherent weaknesses as outlined above. Even that amount of work, however, is not included in the free versions offered for you to use online.

It is also possible for you to spend large amounts of time "prompt engineering" in order to try and game any given LLM "AI" system into producing output that looks better to you.

None of this saves you time, or effort, or money. None of it actually replaces the work of research and writing, unless you choose to use "AI" instead of doing research and writing. While that might make the companies involved very happy, it will not make you a better scholar.

If, knowing the inherent weaknesses of LLM "AI" systems, you choose to add the extra time and effort of interacting with these systems, you will still have to check every output of the "AI" without using the "AI" to do so, against actual sources in pursuit of actual facts.

Worse, given the proliferation of LLM "AI" generated misinformation, even in the course of your actual research and writing, you will have to evaluate recent sources in the suspicion that they may contain generated and so inherently untrustworthy text.

Knowing all of this does not make you immune. No amount of investment in "AI literacy" will make these systems actually trustworthy. No amount of investment in techniques to compensate for inherent errors will make these systems produce factual data instead of generated language.

Because these systems exist and are being offered to—and often imposed upon—you, it is important that you know about them, but it is not important that you learn how to use them, as though your skill could change what they are.

What is important, and where your skills are truly valuable and needed, is your work! It remains important that you do your research, that you talk with your fellow students and teachers, that you learn, and that what you write expresses what you have learned in your own words.