Plagiarism

"Plagiarism" is the word for using another person's literal expressions (words, images, etc.) without citation, 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 is because they are plagiarized, in violation of copyright, from other texts and images by real authors with rights the LLM "AI" companies do not respect.

Lawsuits continue to be filed against these companies in order to force them to obey the law and respect authors and artists from whom they have stolen. Some of those suits are winning, but money and power also compromise the integrity of the legal system.

The JKM Library does not support or recommend the use of LLM "AI" for any purpose. We intentionally disable its implementations in the platforms available to us, because we understand that it is harmful to both librarianship and scholarship.

You may be interested in using this technology, or not. We cannot make the choice for you, in your own work. You may not always be given a choice, depending on the platforms you use. But it is important for you to understand the demands placed on your work by any use you may make of these "AI" systems.

This new technology does not change the nature of plagiarism—but LLM "AI" systems make plagiarism easier than ever, while obscuring the real sources and excluding people from the community of your scholarship.

 

LLM "AI" systems are a source and must be cited

The first and most basic point you need to remember is that LLM "AI" systems are a source, requiring citation just like any other source. Any time you receive output from an LLM "AI" system, whether text, or image, or summary of another text, or editing of your own text, you have interacted with an outside agent whose effect on your work must be understood and cited. No amount of effort you put into your interaction with these systems, no amount of modification you put into your use of the output, changes the fact that its output is not your work.

As with any other source used in your research and academic work, you must know and keep track of what work is yours, and what came from others, where and when and how, and you must cite that clearly every time it is used.

If you receive LLM "AI" output of any kind, and present that output as your own, or use it in other ways without clear citation, you are engaging in plagiarism as a form of academic dishonesty.

You also should know that use of LLM "AI" in your academic work may be a reason that work is rejected. Plagiarism will not hide your use of LLM "AI," and explicit citation at least obeys the ethics of our disciplines, but this is a reason not to use LLM "AI" at all.

 

How to cite uses of LLM "AI" in your work

Because citation is required, we are adapting our existing practices to ensure that your use of LLM "AI" is explicit, thought about, and explained in your work.

This means there are no irrelevant uses of these systems; all effects on your work must be accounted for. You are responsible for keeping careful track of how you have used that "AI" system, where and when, what your prompts were, what the output was, and what exactly you have done with that output. All of these factors will need to be accounted for in citation.

The major style guides have issued guidance as to how, when, and in what ways to cite your use of LLM "AI":

These generally include, beyond mere citation of individual uses, an explicit methodological statement at the front of your work, explaining to your reader how and where and why you have used LLM "AI".

 

Citation does not solve the problems of "AI" text

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. Citation enables your reader to check on the context of material you have cited, verify how you have used it, and expand the community of their own work in the use of yours.

Citing LLM "AI" systems, if you use them, is unquestionably necessary—but not meaningful to your reader in any of these ways. As a source in your work, LLM "AI" output does not exist for anyone else. Even when including your prompt, your citation does not guarantee your reader access to the same output generated for you. Furthermore, its output has no context behind it. There is no way, and nothing, for your reader to check.

When you cite the LLM "AI" system, you are doing something similar to citing an informal conversation with another person. However, LLM "AI" systems are not an authority on any subject matter. You have asked a system to generate statistical text or statistical images based on its training data. The output of any LLM "AI" system is not validated, and is known to be significantly and pervasively in error on even basic factual matters. LLM "AI" is therefore an extremely low-quality source. You should be critical of this kind of source, because of its extremely low quality.

Furthermore, since LLM "AI" systems reproduce training data without telling you, if they do reproduce valid information, that output may actually come from a real source you could have cited—and you will not know what it is, because these systems obscure their sources. In addition to academic penalties, this may create the risk of lawsuit for copyright violation if you publish such material.

Above all, with LLM "AI" as in any other case, citation of a plagiarized work builds on the injustice against the uncredited authors. LLM "AI" systems are being used to replace the work of others you should be reading and citing. The only good solution is to read and cite creditable sources.

 

"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. 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.

 

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 represented in the texts used as its training data, "Large Language Model" systems are not programmed to understand that questions have factual answers, found by direct reference to external data. These system are not programmed to understand the idea of "truth" or "falsehood," or that the language in their training data may correspond well or poorly to realities in the outside world. This also means that LLM "AI" systems do not have any sense of the meaning of the data they have been trained on. Training data does not have meaning, or truth value; it only represents patterns.

LLM "AI" systems are designed to detect and reproduce patterns in response to prompts. Their core concern is the statistical likelihood that similar-looking material—language or imagery—appears in similar patterns elsewhere. These systems are programmed to notice what patterns appear most often in your language, and in their training data, and to manufacture language that looks similar to those linguistic patterns. The programmed assumption is that the most common matching patterns are correct, and that matching those patterns will satisfy your interest. The output of these systems is therefore likely to contain a variety of errors, from the subtle to the absurd, which the system cannot check. And it may even bear no resemblance to reality at all, because the system does not know about reality.

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.

There is therefore some chance that the output of an LLM "AI" system might be correct—if the most common language appearing in the most common pattern of response to the pattern of your prompt in its training data contains language that just happens to correspond to reality, and if artificial weight has not been imposed on some other language. Such answers are generally to trivial, common inquiries—and a facially correct answer is not even always likely to those questions, not to mention the harder and more unique questions of serious academic research.

If the "Large Language Model" does reproduce something that looks like the desired data, in the language it manufactures on demand, it will do so without citation, leaving you ignorant of information vital to your academic work, as well as with no way to check for errors it has almost certainly introduced. 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, by linguistic statistical probability.

You need to beware of your desire to believe language that confirms your assumptions. 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. These systems are designed to change their output until you stop objecting, but they are not designed around matters of fact, so this amounts to manufacturing your consent to non- and misinformation. Correct answers are not part of the programming. Admission of a lack of data is not part of the programming. To the extent that you may have complaints, you are the only standard these systems respond to in your interactions, which will continue generating different text until you cease to object. All efforts to press an LLM "AI" system for depth behind its answers can only result in more generated text, reproducing common linguistic patterns observed in such interactions, purely designed to secure your acceptance of some form of the system's generated output.

All of this makes LLM "AI" systems radically unsuitable for any research work, where you are trying to learn things you do not yet know, especially around matters of fact. 

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.

 

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, creditable sources 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!