10 silent signs that betray the use of artificial intelligence

In the first phase, academic institutions and organizations tried to deal with the new reality using automatic detectors. Software that promised to identify a “signature” of artificial intelligence in texts. But this promise soon turned out to be problematic. The tools were imprecise, sometimes flagging human texts as suspicious, and sometimes completely missing automated products. Gradually, and with a great deal of frustration, the belief that the genie could be put back in the bottle with another algorithm faded away.

Instead a deeper change began to take shape. No longer an attempt to determine who wrote the words, but an examination of the way they were created. Attention has moved from the last line to the entire process. From the way the instructions were read, through the way the document was constructed, to the writer’s ability to explain to himself and others what he actually wrote. From this approach, a series of methods were born, some visible and some almost imperceptible, with one goal: to check whether there is a thinking person behind the text.

The first trap: “The invisible ink”

This trap is based on one of the simplest truths of the digital age, humans and computers do not “see” text the same way. What seems clear and self-evident to us, may be read completely differently by a computerized system. And this is how it happens: the assignment instructions are sometimes sent in an innocent-looking digital file. Within the text, by fairly simple technical means, hidden sentences, white color on a white background, a tiny font or a layer of text that is not visible in normal reading are integrated. The human reader skips over them without knowing they exist. However, when the text is copied in its entirety into an automatic system, all layers of information pass together, even those that were not visible to the eye.

For the lecturers, this is not a trick but a test of reading. Did the student treat the document as a text to be understood, or as a technical input to be passed on. For the students, it is an unplanned lesson about the nature of digital work and the price of giving up critical reading.

The second trap: non-existent sources

One of the most disturbing features of language models is the confidence with which they present information, even when it is completely wrong. In certain assignments there is a reference to an article, book or study whose existence is in doubt, and sometimes does not exist at all. A person trying to locate the source discovers the problem, and sometimes turns to the lecturer or states it explicitly. An automated system, on the other hand, will try to fill in the gaps. She will come up with convincing content, names of researchers, claims and conclusions, everything sounds reasonable, even if it has no basis in reality. For academia, an invented source is not just a factual error. is evidence that the thinking process is interrupted. It’s a painful reminder that artificial intelligence doesn’t distinguish between truth and falsehood, but between persuasive phrasing and less persuasive phrasing.

The third trap: footprints

The transition to live documents, those written and edited in the cloud, changed the power relationship between writer and reader. When the work is submitted with an open edit history, you can see the way it was created. Human writing leaves traces. Deletions, rewordings, paragraphs written at different times of the day. A text that appears at once, complete and polished, tells a completely different story. The test no longer deals with the question of who pressed the keyboard, but whether the document reflects a process of understanding, deliberation and learning. It’s a change that puts even skilled writers to the test, and emphasizes how important the path is as much as the result.

The fourth trap: statistical patterns in writing

Beyond the human eye, there are those who seek to identify the mathematical fingerprint of the text. Automated texts tend towards consistency. Sentences of similar length, uniform rhythm, symmetrical syntactic structures. Human writing, however, is less predictable. It includes jumps, anomalies, and sometimes also a slight disorder that originates from living thought. There is no unequivocal evidence here, but an accumulation of hints. When the text looks too perfect, too balanced, it may arouse suspicion precisely because of that.

The fifth pitfall: formal constraints

Sometimes the test is not in the content, but in the ability to follow the rules. Technical guidelines, a fixed structure for paragraphs, avoiding certain words, are designed to check attention to details. The longer the work, the more difficult it is to keep all the rules at the same time. This means that consistent adherence to constraints is seen as a sign of mastery over the text and deep involvement in the writing process.

The sixth trap: the defense conversation

In the digital age, human conversation returns to center stage. After submitting the work, there is sometimes a short conversation. Not an investigation, but a request for an explanation. What did you mean here, why did you choose this term, how would you explain this claim in simple words. The ability to talk about the text is seen as proof of true understanding, one that cannot be copied.

The Seventh Pitfall: Typical Vocabulary

Language also has patterns. Language models tend to use general, positive and sometimes overly solemn expressions. Concentrated use of them may stand out to an experienced eye. Precise, sometimes even dry language is seen as more authentic than impressive but general formulations.

The Eighth Trap: Translated Syntax

The Ninth Pitfall: Too Predictable Structure

Over perfection may be suspect. An opening that repeats the question, a symmetrical body and a generic summary create a sense of a ready-made template. Meanwhile, “identification” signs that are a stamp of artificial intelligence, such as incorrect punctuation, inversion of letters, linking of English letters to Hebrew letters, may indicate that the text was written by an intelligence. Human writing tends to be less uniform, and sometimes less “beautiful”, but more personal.

The tenth pitfall: sterile design

The look also tells a story. Multiple lists, bullet points and uniform patterns create a technical and alienated look. Flowing text, with small flaws, is sometimes perceived as more human and reliable.