When many people think of AI, they think of online writing tools that help students quickly come up with last-minute essays, or busy content professionals churning out half-baked articles. But AI is much more than just a content generation shortcut.
In fact, new and ongoing developments are helping position AI as a powerful tool in the fight against plagiarism as well. Here are some of the many ways that this unique technology is being used to help educate and inform students, writers, professionals and others throughout countless industries and fighting back against instances of plagiarism.
Advanced Text Matching
Many plagiarism checkers work by comparing a series of chunks of text against existing information in a database. And while this works for directly copied content, it doesn’t work in cases like paraphrased plagiarism or other types of plagiarism.
Using AI allows for more advanced plagiarism checkers to leverage deep learning techniques to identify patterns and look at the structure and sequence of the sentences, even if the words don’t directly match.
For example, writing “Instances of plagiarism are higher as a result of AI writing tools” and “As a result of AI writing tools, the prevalence of plagiarism is higher in academia’ might not use the same words, but both sentences are semantically similar, allowing more modern AI detection tools to catch them.
Plagiarism Detection Across Languages
Another common issue for even well-known plagiarism detection programs is cross-language plagiarism. Bilingual students or professionals can easily lift content in one language, translate it and present it in another, with plagiarism checkers being none the wiser if they only look for information in English.
AI systems that are trained on multiple languages can scan for such instances and detect them using a similar type of text matching as described above, except done across both the original and the translated language.
A-what analysis? When talking about AI and plagiarism, it’s easy to get lost in the weeds with machine learning jargon. Semantic analysis simply means that rather than matching words, the AI works to understand their overlying meaning. It does this by essentially “reading between the lines”. Through its programming, it tries to understand the meaning of words and not just look for patterns on the surface. With this in mind, it does a kind of apples-to-apples comparison of the broader content itself rather than the individual words.
Image and Multimedia Plagiarism
AI has evolved to detect plagiarism well beyond simple text. Using “Convolutional Neural Networks” (which sounds as convoluted as you might imagine), systems can look at the pixel data to find similarities. Just like how AI breaks down the chunks of text to analyze their patterns, so too can it break down frames into images and look at their sequence or pixel data to find areas where they match.
Imagine you’re looking at a photo of a family gathering and trying to find the family dog in the picture. Rather than looking at the whole picture, you’d try to find tell-tale (or is that tell-tail?) signs like the ears or tail. CNNs work similarly.
The same applies to audio through spectrogram analysis. In this case, imagine you’re listening to different signs and trying to figure out their patterns. You can easily distinguish the differences between a bird singing and a person talking, but machines cannot, so tools like spectrogram analysis are used to help them understand what they’re “hearing”.
Ongoing Learning and Adapting
One of the things that sets AI plagiarism detection apart from plain text plagiarism checkers and other types of detection programs is its ability to continually learn and improve over time. Times and technology change and with those changes come ever more inventive ways to try and circumvent or outsmart the system. Artificial intelligence is able to learn from and anticipate these changes and update its detection techniques accordingly.
Integration with Various Databases and Publications
Because AI plagiarism detection can be integrated within various systems and publications, it can automate the process of scanning, storing and indexing content from a wide range of sources. In addition, because it uses NLP (Natural Language Processing) to learn from what it “reads”, it can categorize this information, enabling it to look up and draw from many different sources in seconds.
Personalized Feedback and Education
Rather than simply flagging the potential instances of plagiarism, today’s AI detection services can elaborate on issues it has encountered so that students or content professionals can understand the issues behind their writing as well as how to properly cite the content they’re referencing.
Verifying Against Large Datasets
One of AI’s greatest strengths is its ability to consume, examine, store and pull from massive datasets, enabling entire databases, academic journal repositories and other content sources to be added and used as training materials. This, combined with cloud-based systems enables AI to crawl through considerable amounts of data concurrently, displaying the results in just a few seconds.
In addition by using techniques like hashing, large datasets can be converted into shorter, unique “tokens” that are more efficient for the AI to “digest”. Breaking down large swaths of content in this way makes sure that content is checked thoroughly but quickly.
Here we go back into the weeds of AI jargon again, but don’t worry, it’s painless. Stylometric analysis is like a deep analysis of one’s writing style. Everyone has one, and each one is unique like fingerprints. A stylometric analysis looks at the content creator’s use of sentence structure, punctuation and other stylistic features. If a document suddenly changes its writing style, it might mean that this part wasn’t written by the original author.
Another of the considerable benefits of using AI plagiarism detectors is that they can consistently monitor a given platform so that when new content is uploaded, it can be instantly compared to existing writing in the database and any instances of plagiarism can be detected right away. Well-known plagiarism detectors that leverage AI often use this as one of their biggest selling points, as having a repository of things like student essays or academic reports helps them to root out any paid essay services or similar types of content plagiarism in the system.
One of the most interesting facets of using AI in plagiarism detection is its ability to draw upon patterns, including past instances of plagiarism in terms of the who/what/where/when and how they happened so that it can predict potential future occurrences. Institutions and platforms can then leverage this information to create preventative measures or monitor these areas so that possible future incidents happen less and less.
Last but not least, there’s the academic side of plagiarism detection and AI, which often involves integrating a given AI detection system into the institution’s LMS (Learning Management System). This allows professors to give direct feedback and grade based on any instances of plagiarism found, while helping students scan their own work for plagiarism before turning it in.
Keep in mind that although AI can do all of these things, there’s a definite balance in terms of how much human intervention is needed and how much of the process can be automated. False positives can and do occur, so it’s important to be aware that this can happen no matter how sophisticated the platform is.
The good news is that AI plagiarism detectors have already shown a great deal of promise in being able to go beyond plain text matching. The system’s ability to understand semantics, translate between languages and even go so far as to detect plagiarized pixels makes it an invaluable tool in the fight against plagiarism.
Add to this its ongoing learning and monitoring, and its ability to be integrated into different systems, and you have a comprehensive kit that’s designed to reward originality of thought and true writing skill. With all technologies though, it’s always a good idea to have a human in the loop to consistently ensure that the AI’s results are fair, accurate and balanced and are a true reflection of its capabilities, rather than a simple pattern matching system that barely scratches the surface.
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