Types of AI Content Detectors

Types of AI Content Detectors

The assignment-checking routine has hardly ever been the easiest part of the teachers’ duties, but it has become even more challenging with AI technologies. No one wants to waste time on giving feedback to the text generated by a robot. Hence, educators need tools to distinguish between chatbot output and authentic writing, which is not an easy task. Luckily. AI-empowered technologies work both ways, enabling one to check for AI presence in the text with special tools. How do these instruments work?

There are two types of AI content detectors:

  • rule-based tools;
  • machine learning-based tools.

How exactly do they distinguish between human-written and machine-produced content? Let’s see.

Rule-based AI checkers

As one can conclude from the name, the described type of detector is guided by a set of rules to determine whether the text has been produced by AI. The tool looks for specific characteristics, typical of the AI model, such as repeating patterns, certain vocabulary choices and grammatical structures, inconsistencies, and other traits.

The benefits of rule-based checkers: lightweight and fast work.

The downside of rule-based checkers: often struggle to catch complex content. Also, a high percentage of false positive or false negative results when the set rules are too strict or too lenient.

Machine learning-based AI content detectors

Data-driven algorithms are the pillar of AI detectors based on machine learning. These tools use a number of methods, including statistical analysis, natural language processing, and deep learning, to determine whether the text is generated by AI or written authentically. This type of detection is more flexible compared to the rule-based checkers, as the tool constantly absorbs the new data, improving and learning in sync with the AI models. Hence, the method ensures high accuracy over time.

The benefits of machine learning-based checkers: stay up-to-date due to the constant improvement in sync with the AI-model development.

The downside of machine learning-based checkers: require more computing resources. Sometimes demonstrate less accurate results than the rule-based models.

Final thoughts

Both types of detectors have their pros and cons. To find the golden middle, the AI checker used by Integrito combines the best practices from both methods, incorporating the rule-based approach and the data-driven algorithms to analyze the text. This way, the tool guarantees the most precise results within seconds and allows to reduce false positives, when human-crafted text is flagged as AI output.

Besides, the Integrito report provides so much more! No need to rely on the detector’s score only, and no need to guess–have a look at how the document has been created to see whether the text was written or copy-pasted from a chatbot. Try it now!

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