Tools to recognize texts generated with AI

The use of artificial intelligence to generate texts is increasingly common in areas such as marketing, content writing, education and corporate communication. However, this technology also poses challenges, especially when it is necessary to differentiate texts created by humans from those generated by AI . Fortunately, there are several tools and methodologies to do this.

1. AI text detection platforms

These tools use algorithms and trained models to identify characteristic patterns in automatically generated texts :

• OpenAI Text Classifier : OpenAI's official tool that helps identify whether a text has been generated with models like GPT. It works best with long texts and in English.

• GPTZero : developed for education, analyzes text and calculates probabilities that indicate whether it was written by humans or AI.

• Writer Content Detector : aimed at content professionals, it provides a probability score and reports on possible automated texts.

• Copyleaks AI Content Detector : detects AI-generated texts and checks for plagiarism or similarity with existing content.

2. Algorithms and technical methodologies

In addition to commercial detection tools, experts use AI-based techniques to recognize patterns:

• Perplexity and entropy analysis : AI-generated texts tend to have a very uniform and predictable structure, with less variability than human texts.

• Detection of repetitions and artificial phrases : Automatic models can repeat certain lexical patterns or phrases more systematically than a human writer.

• Style and linguistic coherence : Language analysis tools can detect a “too perfect” or uniform style, which is typical of AI-generated texts.

3. Practical applications

• Education : Teachers can verify whether academic papers have been written with AI assistants, promoting the ethical use of technology.

• Marketing and corporate content : ensure that texts comply with quality and authenticity standards.

• Security and legality : detect textual deepfakes, suspicious automated emails or artificially generated information that may be misleading.

4. Limitations and considerations

• Detection is never 100% reliable , especially with short texts or with very advanced models like GPT-4 or GPT-5.

• Some tools work better with English than with other languages , so detection in Catalan or Spanish may be less accurate.

• AI algorithms continue to improve, and the generated texts become more difficult to distinguish from humans, creating the need for combination with human judgment .

5. Good practices

• Use more than one detection tool to corroborate results.

• Train teams in artificial text recognition and educate about ethics in AI .

• Complement detection with source verification and contextual coherence .

• Implement clear transparency policies when using AI to generate content.