How Do AI Detectors Work? | Techniques & Limitations

As you know, AI tools are being widely used in all industrial sectors, along with academic ones. They have considerably reshaped how students write academic documents. So, this gives rise to several concerns regarding academic integrity, credibility, and originality, as educators and educational institutions require students to use their research and creative writing skills to assess their understanding. 

The pursuit of distinguishing AI-generated and human-written text has given rise to AI detectors. However, one might ask, “How do AI detectors work?”. If you also want to know the answer to this question, you’re in the right place, as this blog extensively discusses the core techniques of AI detectors and their limitations. 

What Are AI Detectors?

AI detectors are defined as the tools or software that analyze written text or documents to determine whether they are written by a human or an AI model. 

With the increasing use of AI tools, such as ChatGPT, Gemini, Copilot, or Jasper to craft assignments, essays, and papers, AI detectors are becoming enormously popular in universities, publishing, journalism, and recruitment. These tools significantly favour originality, accountability, and authorship. 

AI detectors are trained on large datasets of human and AI text and analyze patterns based on that data. They efficiently assign a percentage score or highlight sentences that are “likely AI-generated”. While these tools can’t be deemed 100% correct and reliable, they provide a probabilistic judgment about the writing of an academic paper. 

7 Core Techniques AI Detectors Use

AI detectors rely on a mix of statistical, linguistic, and machine learning strategies and techniques to detect AI-generated text. By understanding these techniques thoroughly, we can learn about how do AI detectors work. Here are the seven core techniques that AI detectors use: 

1. Perplexity Scoring

Perplexity means measuring how predictable a specific text is. When humans write text, it contains various variations and surprises; however, AI-generated text tends to be very fluent and statistically predictable. A high perplexity score indicates “likely human-written”. On the other hand, a low perplexity score indicates “likely AI-generated”. 

Moreover, if a sentence contains very probable words and everything is too perfect, it can also be flagged as AI-generated. 

2. Burstiness Measurement

Burstiness is a technique for measuring variation in sentence structure and rhythm. Human-written text tends to have burty patterns, such as short sentences followed by long ones and variation in tone, rhythm, and style. However, AI-generated text is even, consistent, and uniform in nature. 

High burstiness means that the text is written by a human. On the other hand, less burstiness indicates the involvement of AI generation. 

3. Repetition and N-gram Analysis

This refers to examining repeated word sequences or patterns in the text. When a human writer writes something, they use diverse and unique terminologies and phrases, making the text non-repetitive. AI tools reuse similar phrases and structures repeatedly in long-form text. 

When analyzed by AI detectors, repetitive words are easily flagged as AI-generated. So, whenever you see repetitive phrasing and structure symmetry in a text, it is more likely written using AI tools. 

4. Syntactic and Grammatical Uniformity

This technique of AI detectors analyzes grammatical patterns, parts of speech, and sentence structures. When AI tools write an essay or dissertation, they contain a grammatically correct but rigid syntax. However, human-written text is more diverse and dynamic in nature, including fragments, passive voice, slang, and rhetorical devices. 

So, whenever a paper has too-perfect grammar and consistent structure, it can quickly be flagged as AI-generated. 

5. Semantic Predictability 

Semantic predictability is an advanced technique that measures how likely certain words are given the context. Human-written assignments, essays, and research papers might include idioms, metaphors, sarcasm, or cultural references which AI can’t understand. 

However, AI-written papers contain statistically probable word choices, making it easy for AI detectors to highlight them as AI-generated. 

6. Classifier Models (Machine Learning-Based)

AI detectors also use several machine learning classifier models that are extensively trained on large AI vs. human-written datasets. These classifiers quickly learns and efficiently differentiate between AI-generated and human-written text based on labelled examples.

Moreover, they include various popular algorithms, such as logistic regression, neural networks, and SVMs. A probability score or a binary classification presents the final outcome. 

7. Watermark and Signature Detection

Another excellent technique AI detectors use is watermark and signature detection. They efficiently scan written documents and look for detectable intentional or hidden markers in AI outputs. Whenever a particular text is generated by AI tools, it may leave some patterns, known as watermarks, that are invisible to readers. However, they are detectable by AI detectors. 

This technique is beneficial in those scenarios when AI-generated text is edited and paraphrased after using a remarkable paraphraser. 

How AI Detectors Are Built?

As plagiarism checkers are built based on available data and information available on search engines, so AI detectors, being AI related to machine learning, are built like other modern machine learning systems. These are the stages involved in creating an AI detector: 

1. Data Collection: Large datasets of human-written and AI-generated text are collected and fed to the machine learning models for efficient training. 
2. Preprocessing: The entered text is processed, cleaned, and standardized. This includes steps such as tokenization (splitting into words), noise removal, and normalization of structures. 
3. Feature Engineering: The AI detector’s system understands the features of both AI and human-written text and distinguishes them based on perplexity scores, syntax variance, and repetition rate. 
4. Model Training: A particular model algorithm is trained to label text based on extracted features efficiently. Random Forests, XGBoost, and deep neural network models are commonly used. 
5. Validation & Testing: The AI detector’s trained model algorithm is evaluated by using unseen data and information. It helps to measure false positives, false negatives, and overall accuracy. 
6. Continuous Updating: The AI detector is constantly updated to maintain accuracy and precision, as AI models evolve rapidly. 

However, you can avail yourself of calculus assignment help if you want to get well-written papers, reports, and homework. 

What Happens When You Paste Text in AI Detectors?

Whenever you paste an essay text in a particular AI detector, it undergoes a series of different steps to check originality and uniqueness. Here are the different steps AI detectors take that will further guide you about how do AI detectors work: 

Step 1: Whenever text is pasted, it is subjected to tokenization, which divides the text into small tokens of words and phrases. 

Step 2: Each token is statistically analyzed, and its predictability is checked efficiently. 

Step 3: Then, the pasted text is compared to the already given AI vs. human-written datasets. 

Step 4: Finally, the model assigns a score to the text, which is then presented in a user-friendly format. 

Meanwhile, learn essay writing tips and samples. 

Difference Between AI Detectors Vs. Plagiarism Checkers

Many people can confuse plagiarism checkers with AI detectors; however, both serve different purposes. By understanding their differences efficiently, you can know how AI detectors and plagiarism checkers differ from each other. 

Features	AI Detectors	Plagiarism Checkers
Purpose	They detect AI-generated text in written documents.	They identify copied text from other data sources.
Technology	It uses linguistic modelling and machine learning classifiers.	It involves database research and text matching techniques.
Data Source	They are trained on vast human vs. AI-written datasets.	They are trained on various web and academic content databases.
Output	They showcase outcomes in probability scores.	They showcase outcomes in matched sources and the similarity index.
Use Case	They ensure academic integrity in academic papers, job texts, and publishing.	They are used to ensure academic writing honesty and SEO content originality.

Key Users of AI Detectors

These people require AI detectors for regular use: 

1. Educators & Academic Institutions

The primary users of AI detectors are educators and academic institutions. Professors and universities widely rely on AI detectors to ensure originality in classwork and uphold academic integrity. Moreover, they significantly enable students to use their critical thinking, scholarly writing, and problem-solving skills. 

2. Recruiters & HR Professionals

In the wake of AI writing tools, various job applicants are increasingly using them to complete their tests and write cover letters. However, it destroys the whole purpose of recruitment and assessment. So, recruiters and HR professionals use AI detectors to ensure authenticity and confidentiality. 

3. Journalists & Publishers 

Publishers and media houses highly prefer human-written articles, as the personal perspective and experiences are crucial to building reader trust. So, they widely use AI detectors to ensure text is written by humans or with AI assistance. 

4. Grant and Scholarship Committees

Grant and scholarship committees also use AI detectors. Academic non-profit organizations require students to write motivational letters and personal statements. Undoubtedly, AI tools can quickly generate them in no time. However, AI detectors efficiently help them to ensure fair evaluation. Meanwhile, get personal statement editing services to secure a dream admission. 

5. Freelance & Content Platforms

In the content creation industry, clients require human-written blog posts, articles, and long-form content. So, Freelance marketplaces like Fiverr and Upwork also rely on AI detectors to make sure content creators are not overrelying on AI tools. 

Limitations of AI Detectors

Here are the limitations of AI detectors: 

1. False Positives

The main limitation of AI detectors is that sometimes they can flag perfectly human-written text as AI-generated just because of its fluency, structure, and lack of variance. It is prominent in those cases when the writer is highly skilled and uses polished language. 

2. False Negatives

Sometimes, students use advanced paraphrasers to edit AI-generated text and make it look like human-written text. In that case, AI-generated documents can bypass detection, making AI detectors unreliable and limited. 

3. Lack of Explainability

Another prominent limitation of AI detectors is the lack of explainability. Wherever the AI detector provides a final score, it is only in numerical form or highlighted. It doesn’t give sufficient clarity on why a section is flagged as AI-generated. 

4. Rapid AI Evolution

As you know, AI models like ChatGPT, Gemini, and Claude are constantly evolving and growing more and more advanced. With this, AI detectors also need to be updated to maintain continuous detection quality, but this is not possible with current technology. 

5. Bias Towards Foreign Languages

AI detectors are usually trained on English datasets, so they only favour English-language text during analysis. They efficiently review English-written papers, but penalize non-English text and non-standard writing styles. 

Best Guidelines for Using AI Detectors

Follow these essential guidelines to use AI detectors efficiently: 

🟢 Use as a Supplement, Not a Final Judge AI detectors are just tools. They only indicate, not give absolute verdicts. So, use them only as assistants. Always trust your human judgment and intuition. 🟢 Combine with Other Tools Rather than using them separately, you can use AI detectors by integrating them with other tools like plagiarism checkers. Their combination can provide you with excellent results. 🟢 Efficiently Train Users and Writers Don’t discourage students and writers from using AI tools. Instead, educate them on how to use them appropriately to ensure genuine learning and creativity. 🟢 Disclose the Use of AI Sometimes, AI-assisted work is acceptable rather than AI-generated work. So, thoroughly disclose the AI usage, as educators and publishers value honesty. 🟢 Avoid Over-Reliance Blind reliance on AI detectors can lead to various chaotic accusations. So, make sure to investigate multiple signals, such as tone, research quality, citations, and writing history, before taking any strict action.

Want to Ensure Academic Integrity & Credibility?

Due to various AI tools and detectors available, maintaining academic integrity and credibility has become tremendously challenging. No matter how well students write their assignments, essays, and papers, they are flagged as AI-written. However, you can get professional homework help to overcome such challenges and accomplish your academic goals without any stress and frustration. 

Frequently Asked Questions

Which techniques do AI detectors use to detect AI-written text?

AI detectors widely use perplexity, burstiness, statistical analysis, N-gram analysis, syntactic analysis, semantic predictability, and classifier Models.

Are AI detectors 100% reliable and authentic?

No, AI detectors are not dependable. They provide false positives and false negatives, lack explainability, and are biased towards foreign languages.

Who requires AI detectors to maintain quality and originality?

The educators, recruiters, publishers, scholarship committees, and content platforms widely require AI detectors to ensure originality, uniqueness, and credibility.