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Plagiarism occurs as a result of collective human expression. Wherever people share ideas, there exists a risk that someone will claim them as their own (intentionally or not). Now, with AI in the mix, mitigating that risk is tougher than ever. 

When exploring the topic of plagiarism, Marc Booker, PhD, offers a concrete definition. “Typically, the working definition is using someone else’s work without properly attributing it,” he says. “In the academic context, that means presenting previously created, documented or published ideas as your own original work without proper credit or citation to the original source.”

As the vice provost of strategy at ��ɫ��Ƶ, Booker is well versed in all things academic — even esoteric fun facts. Case in point: Booker says it’s also possible to plagiarize yourself if you don’t acknowledge work you’ve previously created and published or otherwise documented, which is commonly called dovetailing in academic circles.

In the age of artificial intelligence, the ease with which anyone can plagiarize has reached warp speed. This is a big topic, perhaps especially within higher education, not least because the great majority of students now use AI regularly. A 2024 global survey by the of more than 3,800 university students found that 86% said they use AI in their studies, and more than half use it at least weekly. 

Determining how many might be misusing the technology to cheat is tougher to nail down. Though many educators (31%) and administrators (41%) are concerned about academic integrity in the age of AI, students have their worries too. Many (47%) are anxious about copying, and nearly half fret about becoming overly reliant on AI, according to an conducted by Turnitin, a technology that detects plagiarism. 

The biggest question for higher education is simple, if not easy to answer: How can a university be sure students have learned what they need to learn when generative AI tools like ChatGPT™ and Microsoft’s Copilot are so ubiquitous and potentially difficult to spot in a student’s work?

“The point of academics or learning is to show your synthesis or understanding of whatever content you’re consuming,” Booker says. “If you’re entering a question into AI to get an answer, and that is the exact same answer you submit in a course and there’s no synthesis, no learning and no adjustment or application — that’s problematic.” 

So how do students make sure their work never falls under the umbrella of uncredited borrowing? Booker’s answer may surprise you. Rather than avoid AI tools, Booker embraces their potential, from suggesting sources to research to ideation.

“At ��ɫ��Ƶ, we’re not shying away from AI,” he says. “We can’t, because our students are going to be using these tools in their real lives. So, we’re trying to find ways to organically use the tools and put them front and center in assignments, which then allows the student to show the learning that emerges from practical application.”

Humans, after all, have been using devices for centuries to improve or expedite what we create. (The abacus, printing press and calculator are just a few examples.) Artificial intelligence and its relatives are doing the same thing today.

“Gen AI can be very useful in the demonstration of learning,” Booker notes. For example, instead of writing a paper, students might be asked to apply a prompt that asks an AI tool to generate an image of how their lives will be changed after they complete their degree. “A professor could then have the student explain that picture to the rest of the class,” he continues. “There’s a lot of rich learning there that doesn’t tip into plagiarism or unoriginal thought. You’re using a generative tool to get a visual representation of something that can deepen the learning impact and is fun and an interesting way to evaluate self-awareness and identity without writing a static paper.”

Booker shares more examples:

• Students could be asked in a marketing class to write five pitches for a new product. They might prompt generative AI for pitch ideas, then evaluate the best option based on their classroom learning. (If you end up working in marketing, in fact, you’d be doing this very thing.)
• Students taking a leadership course could leverage AI to better understand the predominant leadership models. “You’d take those theories [generated from an AI prompt] and ask it to explain how you would apply them as a leader yourself in certain scenarios to identify which ones resonate with you and your leadership voice. Next, you would cite the source material where you got them from and how you used AI with the prompt,” Booker says. “You would explain to the faculty member which leadership model you would use and why, as well as how these models have changed your thinking on leadership. With this approach, gen AI can unlock new ways of thinking and there’s great learning that can happen, as long as [the source of the information] is disclosed.”

It’s worth mentioning that different courses will have different requirements when it comes to showing and citing one’s work. A math class might stipulate that students need to show how they made computations, for example, while a marketing course or writing-intensive courses might rely on one of the standard citation formats like those found in the American Psychological Association or Modern Language Association. 

What’s considered plagiarism applies to more than just school. “Depending on where you’re at in the work world, different disciplines will think about plagiarism differently,” Booker notes. “If you’re a journalist and try to cite other people’s writing as your own, that’s going to be hugely problematic and impact the credibility of the periodical or organization.”

The arts pose a similar concern. Already, generative AI is raising the question of who (or what) is actually creating a song, image, book or video. If you’re creating something new but borrowing inspiration in the process, it’s important to share the secret sauce on your process. “You need to indicate where [a piece of art] is coming from and how much is your own personal work product versus someone else’s,” Booker elaborates. 

Tech, meanwhile, seems to have fewer constraints. “In the IT space, you can use these generative tools to write computer code, and there are tech organizations that use the AI generative models to write better code,” Booker says. “They don’t really care how someone got from A to B, so that source often doesn’t need to be cited in the same ways unless required to do so by an IT organization’s technical documentation parameters.”

The school-to-workforce pipeline means students are starting to recognize the value of learning how to use AI responsibly and sustainably. A , for example, found that 70% of college graduates said they “believe basic GenAI training should be integrated into courses to prepare students for the workplace.”

Booker acknowledges that these are still early days as in and out of the classroom. Ultimately, though, he sees more upside than down. “Ultimately in academia, the use of AI is primarily about how the tool is used in the input of the process and being able to see the output of learning that happens in between the lines by the student. So AI is not bad in and of itself, it’s how you use it,” he says. “And avoiding plagiarism may sometimes feel complex, but it’s really not. If the words, thoughts or work you are presenting was contributed to or created largely by someone else — or some other thing in the case of AI — just let people know up front.”

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