Plagiarism
How Can AI Detect Plagiarism in Research Papers?
Today’s plagiarism checkers are continuing to advance with AI integrations. Learn how AI is improving the accuracy of plagiarism detection in research papers.
In the academic world, originality and integrity are at the forefront of research. At the same time, concerns about plagiarism in research papers have never been greater.
AI writing tools are creating a vast amount of content, and traditional plagiarism detectors can’t always keep up.
Today’s plagiarism checkers are more advanced than those of yesteryear. They need to stay ahead of AI writing tools that generate human-like text in seconds, which sounds convincingly well-researched.
For instance, the Originality.ai Content Scanner can detect AI writing and plagiarism.
Learn how AI is advancing plagiarism detection through machine learning, natural language processing, and even multilingual plagiarism detection.
First, it’s important to note that just because AI is used to research doesn’t necessarily mean plagiarism is involved.
College students frequently use AI to find sources, references, and studies that they then review further for papers.
The problem arises when that research is passed off as the student’s diligent findings — without making reference to AI or the sources.
With AI continuing to advance and craft sophisticated texts, it’s up to modern plagiarism detectors to uncover plagiarism in research papers.
The first arrow in the quiver of AI in detecting plagiarism comes from natural language processing or NLP.
A study on NLP and plagiarism detection published through the Institute of Electrical and Electronics Engineers (IEEE) notes that natural language processing increases the efficacy of plagiarism checking.
NLP algorithms have the potential capability to interpret patterns, syntax, and semantics in documents. This expands possibilities beyond identifying direct text matches and helps to catch subtle types of plagiarism.
Beyond NLP, AI also leverages machine learning, which, as the name implies, continues to learn and improve over time by training on immense datasets.
According to a study published by the University of Nebraska Lincoln, these algorithms are exposed to extensive quantities of data that contain plagiarism to train them to identify different patterns in plagiarized text.
As a result, machine learning algorithms can spot text similarities that might not be obvious to the human eye. So, if a research paper used more sophisticated types of plagiarism, like paraphrasing, machine learning algorithms could potentially spot this pattern.
Some AI plagiarism detection tools also incorporate the techniques of their more traditional counterparts and compare text across massive databases.
In this case, an AI plagiarism detector could compare a submitted document to millions of essays, research papers, studies, and other academic publications. This enables a detector to flag potential instances of direct plagiarism that could lead to intellectual property issues.
One of AI’s greatest benefits is its ability to scan countless papers in a fraction of the time it would take a human to manually review them.
When real-time scanning is included in plagiarism detection, it gives educators the freedom and flexibility to concentrate their efforts on actual instances of plagiarism rather than the tedious work of comparing line by line across every student submission.
Semantic analysis is an area where AI is lightyears ahead of its classic competitors. Where traditional plagiarism detectors would just look for matching words, semantic analysis digs deeper.
It looks at the meaning of the words, how ideas are expressed, and the relationship between the words and phrases to establish context. So, if a plagiarized assignment is submitted, semantic analysis enables it to pick up paraphrased content, even though the paper uses different words.
Multilingual sources are one of the more difficult challenges for traditional plagiarism detection. However, when AI receives training on multilingual texts, it can help dissolve language barriers.
Some multilingual plagiarism detectors use AI to translate papers or publications written in several languages into a single language. They then employ algorithms to compare the sources to the paper to identify plagiarism.
This allows certain AI-powered plagiarism detectors to seamlessly detect plagiarism regardless of what language the research paper is written in.
One of the greatest features that helps AI detect plagiarism in research methodology and in the research papers themselves is its ability to continually learn and adapt, enabling it to continuously improve plagiarism detection.
This modernizes plagiarism detection so it stays one step ahead of any number of new and clever plagiarism techniques.
The key takeaway? Plagiarism detection is continuing to evolve with the rapid technological advancements of AI.
Originality.ai offers combined plagiarism and AI detection. When you use the Originality.ai Content Scanner you can opt to check for AI, plagiarism, and readability to streamline your editing workflow.
Note: The Originality.ai plagiarism checker is most suitable for content and web publishers, as it only searches Google for sources when checking for instances of plagiarism.