The modern history of plagiarism, including academic plagiarism, can be summarized in a series of major events.

The first is the rise of computing, particularly desktop computing. The second is the spread of the Internet. The third is the development of plagiarism-detection software. The fourth and most recent is the rise of generative AI.

While this is a grotesque oversimplification, viewing the last seventy years of plagiarism through this prism explains how plagiarism has evolved.

Plagiarism used to be an arduous task. Finding and copying content to plagiarize was hard work in and of itself. It was primarily a tool for those unsure about the quality of their work rather than a time/work saver.

But as technology made plagiarism easier, it became a powerful shortcut. This caused more people to commit plagiarism, leading to the rise of plagiarism detection software. That began the cat-and-mouse game that included contract cheating, online essay mills and, today, generative AI.

However, during this time frame, there was a decade-long period where plagiarism was relatively easy to commit, even by accident, but nearly impossible to detect.

But that era did not simply go away. It’s been making headlines as more and more cases of plagiarism from that era are being spotted decades later.

So why was this era such a dark age for plagiarism? It comes down to one problem.

The Previous Dark Age of Plagiarism

It isn’t easy to pinpoint when this era began. Schools and libraries began offering electronic research tools before the Internet was widely available—for example, the first electronic card catalog dates to 1971.

However, the growth of the internet still likely drove the greatest changes. According to the International Telecommunication Union, there were just over 39 million internet users in 1995. That number rose to 1.03 billion in just ten years.

While those numbers are impressive, it’s important to note that academia is likely disproportionately represented in them. Schools not only had access to the internet before many homes did but were the first to adopt high-speed internet access.

This caused significant changes in academic writing. Students and researchers could find information online and easily incorporate it into their work, both ethically and unethically. However, academia was often slow to educate about the internet or set guidelines around its use.

For example, MLA didn’t introduce internet use and citation guidelines until 1999. By then, many students had been using the internet for five or more years. To make matters worse, new editions don’t immediately replace old ones. That’s a change that happens over several years.

It took time for academia to catch up to how the internet changed academic work. I noticed this personally as I was in college between 1998 and 2002.

But, when it comes to plagiarism, the biggest problem during that time was the lack of any way to detect it.

The Rise of Plagiarism Detection

When Turnitin launched in 2000, it didn’t address internet plagiarism or copying. Instead, it was created to address “frat file” plagiarism, which is when students share essays offline.

However, the company quickly recognized the importance of addressing internet plagiarism and added the feature. But even after that shift, it would be years before Turnitin or any plagiarism detection tool became common at schools.

The result is that from the mid-to-late 1990s to the mid-to-late 2000s, internet-based plagiarism was common, but there was no effective detection.

Couple that with the fact that academia was often slow to provide guidance on how to use internet sources correctly, and you have a mess.

When you look at recent academic plagiarism scandals, many involve works from this era. Darryll Pines’ paper was from 2002. Robin DiAngelo’s dissertation was from 2004. Sherri Ann Charleston’s dissertation was from 2008. And so forth.

Some of this is due to the linear nature of time. Academics reaching higher positions today would likely have dissertations from this era. However, there’s also a familiar pattern as many of these cases involve sources that were cited but text that was not.

While this is still a problem, it generally suggests poor writing techniques rather than deliberate academic fraud. To be clear, schools need to address these kinds of cases. However, it is important that we put the cases in the proper context.

As I’ve noted before, this isn’t a DEI issue. It’s an academic one. It points to issues prevalent in academia, especially during that period.

But that’s only half the story. The bad news is that we are likely entering a similar dark age right now.

The New Dark Age of Plagiarism

If all this sounds somewhat familiar, it’s likely because we are hearing echoes of it with generative AI.

Over half of all students have admitted to using AI in college coursework, even though a similar percentage says it is cheating or plagiarism.

However, as we’ve discussed, there are serious challenges when detecting AI writing. While the available tools are improving, they vary widely in capability and even the best aren’t adequate to base an accusation on without additional evidence

Once again, we find ourselves in a period when a new technology that makes plagiarism easier has become widespread, but the means to detect it are limited. It’s also unclear if there will ever be a reliable way to detect AI writing.

But there is some good news. Compared to the late 1990s, academia is responding much more quickly to AI. Standards for citing AI works are already out, and schools are crafting AI policies much more quickly.

While there is still a lot of work to do, we aren’t in the same situation we were 25 years ago.

But that doesn’t change the overarching problem. AI has made cheating and plagiarism easier than ever but also more difficult to detect. While academia seems determined to address the issue, there may only be so much that it can do right now.

Bottom Line

Students who wrote papers before about 2005 probably had no idea what plagiarism detection technology was or that it would ever exist. Now, two decades later, we’re finding issues in works that are decades old.

I wonder the same thing about AI. Though we may never be able to detect all AI writing, there’s a good chance that we will be able to detect today’s generative AI in the future.

That raises a simple question: What will we discover in 20 years? Will this repeat itself in the 2040s?

One thing is for sure, as technology marches, it’s going to change both plagiarism and plagiarism detection. What is impossible today may be commonplace tomorrow.

To borrow a quote that was not from Mark Twain, “History doesn’t repeat itself, but it often rhymes.”

To that end, there’s a lot of rhyming when it comes to academic plagiarism.

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