The fast expansion of digital content creation demands better tools to detect plagiarism now than in the past. Basic plagiarism scanners have known weaknesses that prevent them from finding all forms of similar content. AI detectors offer the solution to this problem. AI detectors use machine learning and neural networks to make plagiarism detection work in ways that far exceed ‘trademark’ detection.
We will present an analysis of AI detectors versus traditional plagiarism checkers using distinct evaluation points to guide you toward selecting the most suitable plagiarism detection tool.
How Do Traditional Plagiarism Checkers Work?
A typical AI detector such as Smodin is string-matching based. This involves the following steps:
- The document under inspection is broken down into small strings of words/phrases.
- These strings are matched against strings derived from the comparison database (e.g., internet pages, academic databases, archived student papers).
- If a match is found, that content is flagged as plagiarised.
- The various instances of plagiarism are highlighted, and an overall plagiarism percentage is calculated.
This system works well to detect text that matches exactly with the source. It achieves optimal results for precise duplicates yet struggles to identify content that has been edited or refined. The system cannot find copied ideas, plus it cannot detect re-used assignments or faulty bibliographic references.
How Do AI Plagiarism Checkers Work?
AI detectors take plagiarism detection to the next level by leveraging capabilities like:
- Semantic analysis: To check similarity in the underlying concepts and ideas even when the text has been paraphrased
- Natural Language Processing (NLP): To analyze writing styles and identify changes between sections within the same document
- Citation analysis: Identifying irregular citation patterns and missing attribution
- Pattern recognition: Identifying anomalies like sudden changes in formatting or writing style
Some AI detectors also use capabilities like:
- Image recognition: Identifying overlaps between images submitted
- Code analysis: Detecting overlaps in code submissions
- Authorship attribution: Linking pieces of writing to probable authors based on writing style
These detectors promise to catch all types of plagiarism by combining various AI capabilities, however well disguised.
Key Differences Between AI Detectors and Traditional Checkers
Detection Accuracy and Capabilities
NLP and deep learning leverage AI detectors that can look for idea plagiarism and spot-check inconsistencies that traditional tools can’t. More extensive plagiarism coverage and accuracy level above 90%, even for paraphrased content.
Traditional checkers use only string matching, missing disguised words, and idea plagiarism. Their accuracy is also lower for paraphrased text, particularly for text with many changes.
Types of Plagiarism Detected
AI detection systems excel at finding seven types of plagiarism not identified by traditional scanners, such as automated tutoring services and repetitive submission of student work.
Ease of circumvention
AI detection software remains effective at finding plagiarism no matter how thoroughly students rewrite or rephrase their content. Standard plagiarism checkers fail to find unauthorized content when writers use substitute words, rearrange phrases or convert text to another language.
Speed and Plagiarism Check Turnaround Times
AI detectors tend to have longer turnaround times because of the extensive checks performed. Traditional tools provide faster results as they rely solely on string matching. Most tools, however, have options for urgent results if needed.
Customization and Versatility
In comparison, AI detectors are more customizable: for instance, you can map custom vocabulary, perform subject-based comparisons, etc. They also have higher versatility in different use cases, such as text, code, and images.
Cost and Pricing Considerations
The high prices per submission of AI detectors are due to their technological complexity. Yet, some vendors are more flexible with pricing according to usage levels and turnaround needs. Traditional tools have simpler and cheaper pricing.
Data Privacy and Security
Strict data security techniques, such as encryption or access controls, are used by AI detectors to protect student data. In the past, traditional checkers have come under some criticism over their data privacy and surveillance concerns.
Industry Adoption of AI-Powered Plagiarism Detection
Despite being a relatively new technology, AI plagiarism checkers are seeing rapid mainstream adoption. According to a recent Verified Market Research report, the plagiarism detection market is projected to grow from USD 465 million in 2024 to USD 795 billion by 2031.
AI and machine learning are expected to fuel much of this 300%+ growth. Education is expected to be the fastest-growing use case, and Asia-Pacific is expected to become the dominant regional market.
Higher education institutions are recognizing the power of AI. In a recent survey by Turnitin, 91% of educators expressed the need for AI capabilities in plagiarism checkers. The need stems from the increasing sophistication of student cheating tactics like contract cheating, essay mills etc.
Many major education technology platforms have already integrated AI detectors, like Blackboard’s SimCheck, Canvas’s Turnitin, Moodle’s Urkund integration and more. Their widespread university acceptance underlines that AI is the future of plagiarism detection.
Comparing Leading AI Detector Products
Let’s briefly compare some leading AI-powered plagiarism checker products:
Copyleaks
- Claims to detect plagiarism with over 90% accuracy using AI
- Offers specialized capabilities for content marketing and businesses
- Has graded pricing plans based on the number of monthly submissions
- Has cloud scanner, MS Office integration, and HR email alerts
Unicheck
- Uses natural language processing to detect idea plagiarism
- Has features like LMS integration, batch upload, and API access
- Provides document similarity reports for each submission
- It is used by academic institutions and businesses
Quetext
- Combines AI with forensic linguistics for comprehensive detection
- Specializes in identifying contract cheating patterns
- Provides features like bulk upload and API integrations
- It is used widely across universities and colleges
StrikePlagiarism
- Claims to use AI to perform 13 different plagiarism checks
- Provides both web interface and native integrations
- Gives detailed similarity reports, including citation analysis
- Has customized solutions for universities and publishers
These products compete on accuracy levels, feature sets, use case coverage and scalability. However, they are uniformly more advanced than traditional tools in detecting disguised and unintended plagiarism.
Conclusion
Plagiarism detectors powered by AI capabilities, such as semantic analysis, natural language processing, pattern recognition and image recognition, offer dramatically better accuracy and much wider reach than traditional plagiarism detectors. These tools find both hidden and accidental plagiarism that students use in their academic work.
Industry reports show AI technology now dominates educational institutions because students use advanced cheating methods. The plagiarism detection tools Smodin, Copyleaks, Unicheck, Quetext, and StrikePlagiarism work hard to enhance their ability to find duplicate content.
Today, such AI systems must deal with long required processing times and increasing budget needs. Better technology and improved business efficiency are fixing these issues for the industry.
AI holds the future of plagiarism detection as it’s capable of detecting copied content as much as human experts can.
