The Complete AI Detector Guide for 2026: How It Works, Top Tools, and What You Need to Know

Featured blog Artificial Intelligence

27th Apr 2026

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24 mins

Mansi Porwal

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Table of Contents

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1. Why AI Detection Matters in 2026? 
2. Key Takeaways 
3. What Is an AI Detector?  
4. Are AI Detectors Accurate? What the Data Shows 
5. Best AI Detectors Compared: Top Tools for 2026 
6. AI Detector in Practice: A Real-World Detection Scenario 
7. Which AI Detector Should You Use? A Decision Framework 
8. Who Needs an AI Detector? Use Cases by Audience 
9. Can AI Detection Be Bypassed? 
10. How to Use an AI Detector: Step-by-Step 
11. Free vs. Paid AI Detectors: What’s the Difference? 
12. The Future of AI Detection: What’s Coming Next 
13. Choosing the Right AI Detector 
14. Frequently Asked Questions About AI Detectors 
15. Sign Up for Quetext Today!

Author Bio

Mansi Porwal

At Quetext, we are dedicated to empowering writers, students, educators, and professionals with tools that foster originality and creativity. As a leading plagiarism detection and writing assistance platform, we bring insights on ethical writing practices, innovative AI technologies, and practical solutions for crafting impactful content. Our blog is curated by a team of passionate experts who share valuable tips, industry updates, and strategies to help you write with confidence and authenticity. Whether you're looking to refine your writing skills or understand plagiarism nuances, Quetext is here to guide you every step of the way. Together, let’s embrace the power of original writing.

Why AI Detection Matters in 2026? 

An AI detector is a software or tool that can give you an indicative score to check if your text was generated by a human or by an artificial intelligence system like ChatGPT, GPT-5, Google Gemini, or Claude. Quetext has an AI detector that uses NLP and other technologies to analyze text for authenticity and combines that with additional writing features like plagiarism detection, grammar checker, AI summarizer, AI humanizer, citation generation and so on. By doing this, Quetext creates one tool for students, teachers, and publishing professionals to use to establish the authenticity of both written and non-written works. 

Many people and companies are looking for an effective way to be able to identify AI-created content in their industry. The demand for this type of detector has increased significantly since the beginning of 2023. At this point in time, AI writing assistants produce much of the content that is put online, submitted for academics, and made available through freelance writing.  

Anyone who reads or watches content on the internet will wonder whether it was created by a human or by AI. When educators assess student work products, when publishers evaluate freelance authors’ work products, and when institutions attempt to maintain academic integrity, the need to be able to identify AI-generated content has gone from being a theoretical question to being a practical necessity. 

This guide will provide a description of how detecting AI-generated content works, compare AI detection tools to determine which are most accurate as of 2026, identify who will use these tools, and provide an objective look at what these tools can and cannot do.
Try Quetext’s AI Detector free to see the analysis in action.

Key Takeaways 

• AI detectors identify AI-generated text by measuring linguistic patterns, primarily perplexity (word predictability) and burstiness (sentence length variation). 
• The most accurate tools in 2026 achieve 85–99% accuracy on unedited AI text, but accuracy drops significantly once text has been paraphrased, edited with little changes, written in different languages or humanized. 
• False positives are a documented problem: Stanford HAI research found 61.3% of non-native English speakers’ essays were flagged as AI-generated by leading tools. 
• No single tool is universally reliable; the right choice depends on your use case: student self-checking, educator batch scanning, or publisher verification. 
• Quetext combines AI detection with plagiarism checking, grammar correction, AI humanizer, AI Summarizer and citation generation, one scan covers multiple integrity concerns. 

What Is an AI Detector?  

An AI detector is a software that analyzes existing patterns in written text to determine if it was written by a human or an AI language model. These tools measure two core metrics: perplexity which means it tries to understand how statistically predictable each word chosen by the writer is and burstiness, which means the degree of variation in sentence length, tonality and structure. In short, humans cannot use the same word choice throughout the content piece, and no human can maintain the same burstiness as well.  

AI detectors rely on this quality of human writing to give the AI score. AI-generated text scores low on both: word choices are predictable; sentence lengths are uniform. Human writing shows more variation in both measures. Modern detectors supplement these metrics with machine learning classifiers trained on millions of verified AI and human text samples. Leading tools in 2026 can identify outputs from ChatGPT (GPT-3.5 through GPT-5), Google Gemini, Claude, Llama, and Mistral with accuracy rates ranging from 85% to 99% on unedited text. 

How Do AI Detectors Work? 

AI detectors work by analyzing the statistical and structural patterns that distinguish machine-generated text from human writing. Two core metrics form the foundation of most approaches, perplexity and burstiness, supplemented by ensemble machine learning classifiers. For a deeper technical breakdown, see how AI detectors analyze text.

Perplexity Scoring 

Perplexity measures how predictable a piece of text is according to a reference language model. When an AI system generates text, it selects words that are statistically most likely given the preceding context; the output is highly predictable, or “low perplexity.”  

Human writers make more unexpected choices: an unusual metaphor, an abrupt tonal shift, a sentence that breaks convention emphasis. AI detectors exploit this by running candidate text through a reference model and scoring its predictability. A consistently low perplexity score is the primary signal of machine authorship. 

Example: The AI-generated sentence “The experiment yielded significant results consistent with prior research” scores low perplexity; every word is exactly what a language model would predict. A human might instead write “The results surprised us, though in hindsight they probably shouldn’t have” higher perplexity, more distinctively human. 

Burstiness Analysis 

Burstiness refers to the natural variation in sentence length, structure, and complexity that characterizes human writing. Humans naturally mix short, punchy sentences with longer, more complex ones, often within the same paragraph. AI-generated text, particularly from GPT-4 and Gemini, tends toward a more uniform rhythm and sentence length. Detection tools measure this variance: low variance in sentence complexity combined with low perplexity is the clearest combined signal of AI generation. 

Machine Learning Classifiers 

Modern detection tools are now more than just methods to measure Determines Pertaining to Perplexity and Burstiness. They produce classifiers that have been trained on large datasets of valid horsepower for human-generated Text and AI-generated Text. These classifiers can detect more subtle patterns/styles of writing than either burstiness or perplexity alone.  

Writing styles are always changing due to continuous advancements in AI Development, as well as constant updates to models of text generation (i.e., updates to both GPT-4 and Gemini). Consequently, the most accurate tools continually update their classification techniques. Thus, an ongoing “arm race” exists between AI-generated and AI-detected Text, which has posed the premier challenge for those working within the field of AI Development. 

What AI Models Can Be Detected? 

The major AI models detectable by leading tools in 2026 include: ChatGPT (GPT-3.5, GPT-4, GPT-4o, GPT-5), Google Gemini (1.0, 1.5, 2.0), Anthropic Claude (2, 3, 3.5), Meta LLaMA, and Mistral. Quetext’s AI Detector is trained on outputs from all major commercial models. Obscure open-source fine-tuned models are harder to detect reliably, an honest limitation worth acknowledging for researchers working with niche models. 

Are AI Detectors Accurate? What the Data Shows 

AI detector accuracy on unedited AI text averages 85–99% across the top tools in 2026. This figure drops substantially once text has been paraphrased, manually edited, or passed through a humanizer. Understanding both the headline numbers and where they break down is essential for applying these tools fairly. 

Accuracy Benchmarks by Tool

The False Positive Problem 

The most significant documented limitation of AI detectors is their rate of false positives, flagging human-written text as AI-generated. Research from the Stanford Human-Centered AI lab found that 61.3% of non-native English speakers’ writing samples were flagged as AI-generated by leading detection tools, compared to significantly lower rates for native English speakers. This disparity occurs because non-native writers often use simpler, more predictable vocabulary, the same characteristics that detectors associate with AI output. Educators and publishers who apply AI detection without understanding this bias risk unfairly penalize legitimate writers. 

Quetext addresses this by presenting contextual confidence scores at the sentence level, rather than a single binary verdict. This lets reviewers examine which specific passages triggered concern and make a more informed judgment, rather than treating an overall percentage as conclusive proof. 

Limitations and Edge Cases 

At this time, all Current AI Detectors cannot be 100% accurate, which means when scanning documents that have been created by humans and AI, you will likely get a false overall score often. In addition, AI Detection Tools tend to have less accuracy when using small sample sizes (less than 100 words). For example, if you ask for an AI piece that has been paraphrased by the AI or an Original AI When finished, the results from the detector will be way off than if the same script had not been reviewed and revised by a human. Although there are many limitations associated with AI Detection, the AI Detection should still be used as a verification tool, and should only be considered, not used to make a final judgment. 

Best AI Detectors Compared: Top Tools for 2026 

The best AI detectors in 2026 are Quetext, GPTZero, Originality.ai, Turnitin, Copyleaks, Winston AI, and Sapling AI, each optimized for a different primary use case. The table below covers the criteria that matter most for practical decision-making.

Quetext AI Detector stands apart from standalone tools by offering AI detection alongside plagiarism checking grammar correction, AI humanizer, and citation generation on a single platform. Its free tier supports 1,000 words per month, sufficient for most students to self-check before submission. The Professional plan at $19.98/month adds bulk scanning of up to 100 files simultaneously, practical for educators and editorial teams. 

GPTZero It was the first widely adopted AI detector built specifically for education, and it remains a benchmark for the category. Its free tier is genuinely generous at 10,000 characters per month, and its institution-facing product integrates with common LMS platforms. Where it shows limitations are on edited or paraphrased AI text, accuracy drops more steeply than Quetext or Originality.ai on humanized content. 

Originality.ai is the preferred tool for professional publishers and content agencies. Its per-credit model ($0.01 per 100 words) scales well for teams scanning large volumes, and team management features and API access distinguish it from individual-use tools. The per-credit pricing can add up quickly at high volume without moving to the flat monthly plan. 

AI Detector in Practice: A Real-World Detection Scenario 

Understanding what an AI detector flags, and why, is clearer with a concrete example. Below is a realistic scenario showing the same content before and after detection, followed by a human-revised version.

The Original AI-Generated Passage 

Source text (AI-generated by GPT-4): “Climate change is one of the most pressing issues facing humanity today. The consequences of rising global temperatures include increased frequency of extreme weather events, rising sea levels, and widespread disruption to ecosystems. It is essential that governments, businesses, and individuals work together to reduce greenhouse gas emissions and transition to renewable energy sources.” 

Quetext AI Detector result: Overall AI probability score: 94%. All three sentences are flagged with high confidence. Perplexity score: very low (word choices are maximally predictable throughout). Burstiness score: Near-zero (all sentences are similar in length and follow identical Subject → Verb → Object structure). The phrase “one of the most pressing issues” is among the most common AI-generated constructions across all major models. 

The Revised Human-Written Version 

Revised version: “My students keep asking whether climate change is really that bad, and the honest answer is that it’s worse than the headlines suggest. Sea levels are already measurably higher than they were in 1990, and the 2023 wildfire season in Canada alone released more carbon than France emits in a year. Fixing this isn’t just a policy question; it’s a coordination problem on a scale we haven’t attempted before.” 

What changed: The revised version introduces an unexpected narrative framing (a teacher’s students), uses a specific data point (2023 Canadian wildfire emissions) rather than a vague general claim, and closes with an analytical insight that resists easy categorization. These choices raise perplexity and burstiness simultaneously. A Quetext scan of this revised passage would typically return an AI probability below 20%. 

What does this show for Practical Use? 

This scenario illustrates two practical uses of an AI detector. For educators: the original passage is a clear flag worth investigating further. For students: if your own human-written work reads the original, predictable structure, generic claims, no specific data, it may trigger a false positive. Running it through Quetext before submission lets you identify and revise those passages before they become a problem. 

Which AI Detector Should You Use? A Decision Framework 

Choosing between AI detection tools comes down to three factors: your volume requirements, whether you need additional features alongside detection, and your budget. The rules below are based on actual feature comparisons, not general recommendations. 

Use Quetext AI Detector When… 

• You need AI detection and plagiarism checking in a single scan, Quetext is the only tool in this list that combines both 
• You are a student who wants to self-check before submission and needs a free option without creating an account 
• You are an educator who needs to scan up to 100 files at once (Professional plan bulk scan) 
• You want sentence-level confidence scores rather than a single percentage, Quetext’s output shows exactly which passages triggered detection 
• Budget is a constraint, at $9.99–$19.98/month, Quetext is competitively priced against GPTZero’s paid tiers

Use GPTZero When… 

• You are an educator at an institution that has adopted GPTZero through a formal LMS integration 
• You need the most generous free tier available, 10,000 characters/month is more than Quetext’s 1,000 words/month for free users 
• You primarily scan unedited, paraphrased student submissions where GPTZero’s 99% accuracy on raw AI text is most relevant 

Use Originality.ai When…

• You are managing a content team or agency and need multi-user access, shared reporting, and API-level integration 
• You scan very high volumes of content and prefer per-credit pricing over flat monthly subscriptions 
• You do not need plagiarism checking integrated. Originality.ai’s team features are its differentiation, not all-in-one functionality 

Use Turnitin When… 

• You are at a university or institution that already licenses Turnitin for plagiarism checking, adding AI detection costs nothing extra 
• You require LMS integration (Canvas, Moodle, Blackboard) with audit trail documentation for academic integrity cases 
• Institutional procurement is not a barrier; Turnitin is not available for individual purchase

Quick rule of thumb: Individual users and students → Quetext (free tier, all-in-one) or GPTZero (most generous free tier). Small teams and publishers → Quetext Professional or Originality.ai. University-level institutions → Turnitin if already licensed, Quetext Professional otherwise. 

Who Needs an AI Detector? Use Cases by Audience 

The value, workflow, and ideal tool selection differ significantly depending on whether you are a student protecting yourself from false positives, a teacher scaling detection across a class, or a publisher maintaining editorial credibility. 

AI Detectors for Students 

For students, the primary value of an AI detector is self-verification before submission. Running your own work through a detector beforehand eliminates the uncertainty of being flagged unexpectedly. Quetext’s dedicated guide on AI detection for essays explains how essay-specific detection works, including why formulaic academic phrasing and predictable sentence structures can trigger false positives in 100% human-written work, particularly for non-native English speakers. 

AI Detectors for Teachers and Professors 

Educators need detection tools that scale efficiently. Quetext’s bulk scan feature supports up to 100 files simultaneously on the Professional plan, directly addressing the time cost of checking a full class submission. For guidance on integrating detection into grading workflows and building a policy that is both enforceable and fair, see best AI detectors for educators. 

AI Detectors for Content Creators and Publishers 

Publishers that are vetting freelance content need a way to detect false negatives quickly and accurately at scale when they find out that they have published something in error; they risk their editorial credibility if they publish AI generated content as if it were written by a human. 

Each publisher needs the ability to check for plagiarism at the same time as they check for AI-generated content. The Professional plan from Quetext allows bulk scanning of documents and checks for both AI attribution and source-level originality before inserting in a single workflow. Originality.ai provides API and team management features, both of which may be beneficial for high-volume pipelines.  

Read More here: Best AI Detector Tools for Content, Images & Videos 

AI Detectors for Institutions 

University-level adoption typically requires LMS integration, institutional licensing, and audit-trail documentation. Turnitin dominates this space, having added AI detection to its plagiarism-checking infrastructure in 2023. For smaller institutions that cannot access Turnitin’s enterprise pricing, Quetext’s Professional plan offers bulk scan capability and per-document reporting as a practical alternative. 

Can AI Detection Be Bypassed? 

The question should be, should we try to bypass AI detectors? 
Yes, AI detection can sometimes be bypassed using paraphrasing tools, AI humanizers, or manual editing. But that is not the right approach we should have as a writer. Modern detectors are increasingly resilient to these techniques, however, and the practice carries serious academic and professional consequences. For a full analysis of bypass detection techniques and why they increasingly fail, Quetext’s dedicated post covers both the technical and disciplinary dimensions. 

Common bypass methods include running AI text through a paraphrasing tool to redistribute vocabulary and sentence structure; using dedicated AI humanizer services; manually rewriting flagged passages; and prompt engineering (explicitly instructing the AI to mimic a specific writing style). Each approach reduces detection accuracy to some degree; none eliminates it entirely. 

The fundamental challenge is that statistical traces remain even after bypass attempts. Paraphrased AI text shows higher perplexity than raw AI output but is still lower than typical human writing, giving detectors a detectable middle ground. Ensemble classifiers that run multiple detection models simultaneously are particularly resilient to single-method bypass approaches. As detectors incorporate training samples of humanized and paraphrased AI text, the window for reliable bypass continues to narrow. 

How to Use an AI Detector: Step-by-Step 

Using an AI detector effectively takes up to two minutes. The following walkthrough uses Quetext as an example. 

• Step 1: Navigate to quetext.com/ai-detector or create a free account. No credit card is required for the free tier (up to 500 words per month). 
• Step 2: Paste your text directly into the input field, or upload a .docx or .pdf document. For best results, use the complete document rather than excerpts; detectors are more reliable on longer samples (300+ words). 
• Step 3: Click “Check for AI” and wait 10–30 seconds. Processing time scales with document length. 
• Step 4: Review the overall AI probability score at the top of the results panel. This figure reflects the tool’s confidence that the text is AI-generated, expressed as a percentage. 
• Step 5: Examine the sentence-level highlighting. Quetext highlights individual sentences flagged with the highest AI probability, letting you isolate specific concerns rather than treating the full document as a single verdict. 
• Step 6: Interpret the confidence scores in context. Scores above 90% on substantial passages represent strong evidence of the AI generation. Scores below 30% are generally consistent with human-written text. Scores in the 50–75% range warrant closer review of the flagged sentences specifically. 

Free vs. Paid AI Detectors: What’s the Difference? 

It majorly depends on the access you. The core detection engine is largely the same between free and paid tiers; the meaningful differences are word limits, batch processing capacity, API access, and LMS integration. Understanding these trade-offs avoids overpaying for features you do not need. 

Free tiers are functional for occasional use: Quetext provides 500 words per month; GPTZero offers 10,000 characters per month, and several tools (Sapling, ZeroGPT) offer free single-scan access with reduced feature sets. The limitation of free tiers is not primarily accuracy; it is volume. A teacher with 30 students submitting 800-word essays cannot realistically operate on a free tier. For a detailed comparison of what free AI detection options actually offer, including hidden caps and feature restrictions, see Quetext’s dedicated breakdown. 

Paid plans unlock batch scanning, longer document support, API access, team management, and priority updates as new AI model outputs emerge. Quetext’s Essential plan at $9.99/month suits individual heavy users; the Professional plan at $19.98/month adds bulk scan (100 files simultaneously) for educators and editorial teams. 

The Future of AI Detection: What’s Coming Next 

AI detectors is just at the beginning of solving a problem. The most significant near-term advances are likely to come from provenance-tracking technology rather than post-hoc pattern analysis. Watermarking is the most discussed approach: OpenAI has implemented C2PA (Coalition for Content Provenance and Authenticity) metadata standards in DALL-E outputs, and similar watermarking for text generation is under active development. If language models embed cryptographic signals in their outputs, detection could shift from probabilistic inference to verifiable proof. The complication is that open-source models can be modified to remove watermarks. 

Multimodal detection, identifying AI-generated images, audio, and video alongside text, is an expanding frontier. Tools like Winston AI and Hive combine text and image detection on a single platform. The regulatory dimension is also shaping the space: the EU AI Act, which entered enforcement phases in 2025, includes disclosure requirements for AI-generated content in certain contexts, adding legal weight to detection practices for organizations operating in Europe. 

The broader trend is clear: content authenticity verification will become a multi-signal process combining AI text detection, plagiarism checking, provenance metadata, and media analysis. Platforms that consolidate these functions have a structural advantage over single-purpose tools, which is the direction Quetext’s all-in-one model is positioned for. 

Choosing the Right AI Detector 

Choosing the right AI detector depends on who you are and the size of your organization. Students will experience the greatest benefit from AI tools that have sentence-level highlighting and honest confidence levels associated with their estimations. It is far more important for students to understand why they are flagged for AI detections than how well the detection tool can determine AI results.  

While educators require batch processing capabilities with clear scoring that can withstand a student’s challenge to a classification result, publishers and content teams need a detection process that will provide speed, volume, and integration into their respective workflows. 

Quetext provides a strategically located point of intersection by providing comparable AI detection with plagiarism checking, grammar correction, AI humanizer, and citation generating capability all within the same online platform, at a price suitable for individual users and small institutions.  The reasonable expectation of this tool is that there are no completely reliable AI detection capabilities; however, when using multiple evidence-of-detection signals, sentence-level confidence ratings, and ongoing classifier updates in detecting AI use in 2026, Quetext detection estimates tend to be one of the better quality and preferable options among all current tools on the market for AI detection. 

Start with a free scan at Quetext’s AI Detector, no account required and see exactly how your document scores before making any further decisions. 

Frequently Asked Questions About AI Detectors 

What is the most accurate AI detector in 2026? 

As of the year 2026, the highest-performing AI text detection tool without any editing or manipulation is expected to be Winston AI (99.98% claimed accuracy), Copyleaks(99.12% claimed accuracy), and by using education-specific benchmarks GPTZero(GPTZero 99%).  

Most likely Quetext will consistently achieve 98%+ accuracy with respect to the raw output of GPT4 and Gemini models of text generation, however; these solutions are assumed to be accurate on only unedited AI generated texts since 65%-82% of these tools report significantly lower levels of accuracy when evaluating<paraphrased, edited/modeled and/or humanized versions of text generated by AI systems.>  

• Winston AI and Copyleaks are among those products that present proof of the highest number of equally accurate results by which to consider using them; yet independent verification methods are not available for these detection solution providers. 
• Quetext, GPTZero, and Turnitin show significant independent benchmark results of their respective performance indications. 
• Paraphrased or humanized content will be detected with meaningfully lower levels of accuracy than the percentage reported in headline claims. 

Can AI detectors detect ChatGPT? 

Of course, all major AI detectors can identify text from all LLM’s including GPT-3.5, GPT-4, GPT-4o, and GPT-5. ChatGPT’s outputs are well-represented in detector training data due to its widespread use. GPT-5 is detectable but with marginally lower confidence, its outputs have higher perplexity and more varied sentence structure, narrowing the statistical gap with human writing. Sample length matters: texts over 300 words yield significantly more reliable detection than short excerpts. 

• GPT-3.5 and GPT-4 are the most reliably detected ChatGPT versions 
• GPT-5 detection accuracy is slightly lower due to more human-like text characteristics 
• Short samples under 100 words produce unreliable results across all tools

Do universities use AI detectors? 

Using AI detectors is becoming the need for the hour. All major universities now incorporate AI detection into their academic integrity workflows. Turnitin is the most widely adopted institutional tool, having added AI detection to its plagiarism-checking infrastructure in 2023 but the false positive rate for it is quite high. GPTZero and Quetext are gaining adoption among individual instructors and smaller institutions. An NPR education AI report (December 2025) found significant variation in how universities communicate their AI detection policies and enforcement procedures to students. 

• Turnitin is the dominant institutional AI detection tool 
• GPTZero and Quetext are widely used by individual educators 
• Institutional policies vary significantly, students should review their institution’s stated policy

Are AI detectors free? 

Most AI detectors offer a limited free tier. Quetext’s free plan provides 500 words per month with no credit card required. Free tiers are generally sufficient for occasional personal use, a student for self-checking a single essay, but are insufficient for educators or publishers managing high content volumes. 

• Quetext: 5000 words/month free, no credit card required 
• GPTZero: 10,000 characters/month free 
• Paid plans start at approximately $9–$15/month for individual users with higher volume needs

Can AI detection be wrong? 

Yes, all AI detectors produce false positives (flagging human writing as AI) and false negatives (missing actual AI text). The Stanford HAI research documented that 61.3% of non-native English speakers’ essays were flagged as AI-generated, a significant false positive rate with real consequences for affected students. Detection accuracy also degrades samples, mixed-authorship documents, and paraphrased text. You should treat the detector’s output as a starting point for review, not a definitive verdict, is the responsible approach. 

• False positives disproportionately affect non-native English speakers 
• Short texts under 100 words and paraphrased content reduce reliability across all tools 
• Sentence-level confidence scores provide more context than a single percentage flag

What’s the difference between a plagiarism checker and an AI detector? 

A plagiarism checker helps you check your content against a database of existing published sources, websites, academic papers, and previously submitted documents, to identify matching or highly similar content. On the other hand, an AI detector helps you understand which part of your content sounds like it’s written by AI and which is written by human. It analyzes the internal statistical patterns of the text to determine whether it exhibits AI-generation signatures. The two tools address different integrity concerns and are most effective when used together. Quetext is one of the few platforms providing both functions in a single scan. 

• Plagiarism checker: compares text against an external source database to find matches 
• AI detector: analyzes internal text patterns for statistical signatures of machine generation 
• Quetext provides both in one platform, one scan covers both integrity concerns

How do I check if my essay will be flagged as AI? 

You need to shortlist an AI detection tool like Quetext and run your completed essay through it before submission. Quetext’s free AI Detector provides sentence-level highlighting showing exactly which passages are flagged, letting you review and revise specific sections before submitting. Pay particular attention to formulaic or templated passages, even in fully human-written text; these are most likely to trigger false positives. 

• Scan before submitting, not after, to identify potential flags while you can still revise 
• Review sentence-level highlights rather than just the overall score 
• Revise formulaic or unusually uniform passages that the detector flags with high confidence 

Can AI detectors detect images generated by AI? 

There are dedicated AI image detectors in the market.  Some AI detection tools have expanded into AI image identification as well. Winston AI and Hive include image detection alongside text analysis, identifying outputs from Midjourney, DALL-E, and Stable Diffusion. AI image detection accuracy is generally less reliable than text detection, and detection of AI-generated video and audio remains in early-stage development. For users with image detection needs, they should opt for dedicated tools for AI image detectors.  

• Winston AI and Hive offer combined text and image AI detection 
• AI image detection is a growing capability but less mature than text detection 
• Video and audio AI detection is still in early development across all platforms