The Ten Telltale Signs of AI-Generated Text
A practical guide to recognizing and refining machine writing patterns
To accompany the following blog post, I’ve created an “AI-ism Detection Bingo” card that educators can use as an interactive exercise in their classrooms. The bingo format transforms what might be a dry lesson on textual analysis into an engaging activity where students actively hunt for the patterns described below. Teachers might have students apply the card to various texts, both AI-generated and human-written, to develop their critical reading skills and pattern recognition abilities. The game format also helps students remember these patterns more effectively than they would through passive reading alone.
Beyond Detection: A Philosophy of Refinement
As artificial intelligence becomes increasingly integrated into our writing practices, the distinctive patterns of machine-generated text have become more apparent to careful readers. These stylistic fingerprints emerge not as deliberate markers but as the natural consequence of how large language models process and generate text. The purpose of identifying these patterns extends beyond simple detection; understanding these telltale signs helps writers who use AI assistance craft more engaging and authentic prose.
In my own writing practice, I regularly observe and correct these patterns, not to evade detection algorithms but to create text that resonates more naturally with readers. I generally do not use AI detectors, as I consider the use of AI in improving one’s writing within the context of scholarly communication to be an appropriate and valuable application of this technology. I am a thinker, not a natural writer. Without the editorial help of AI, my writings would not be enjoyable to read. The technology helps me translate complex ideas into accessible prose while maintaining academic rigor. And for full transparency, I maintain a detailed ethics statement about my use of AI in writing, which can be found here on the Augmented Educator Substack blog.
The goal in studying and correcting AI patterns in writing must be refinement, not deception. When we understand how AI tends to structure its output, we can better collaborate with these tools while maintaining our distinct human voice. With this principle in mind, I’ve compiled the following ranked exploration of the ten most significant indicators of AI-generated text, progressing from the subtle to the unmistakable.
The Overuse of Transitional Phrases and Hedging Language
Starting with one of the more subtle indicators, AI-generated text often exhibits an excessive reliance on transitional phrases and hedging language that creates an overly cautious tone. Phrases such as “it’s important to note that,” “generally speaking,” “to some extent,” and “from a broader perspective” appear with disproportionate frequency in machine-generated content. This tendency stems from the safety protocols embedded in language models during their training process. These systems are programmed to avoid making absolute statements that might be incorrect or controversial, leading to a consistently tentative voice that rarely commits to strong positions.
The hedging habit manifests not just in explicit qualifiers but in the overall structure of arguments. Where human writers might confidently assert a position based on evidence or experience, AI tends to present multiple perspectives even when unnecessary, creating text that feels perpetually balanced to the point of becoming wishy-washy. This pattern reflects the model’s training to be helpful and harmless, but it strips writing of the conviction and authority that makes prose compelling. Human writers naturally vary their level of certainty based on context and evidence, while AI maintains a uniform cautiousness that becomes predictable after extended reading.
Predictable Sentence Templates and Structural Monotony
Moving up in significance, AI-generated text relies heavily on formulaic sentence structures that create a rhythmic monotony. Common templates include the “From X to Y” construction for describing ranges or variety, such as “From bustling cities to serene landscapes” or “From beginners to experts.” Another frequent pattern is the heavy use of present participial phrases, structured as a main clause followed by a comma and an -ing verb phrase, like “The system analyzes the data, revealing key insights.” Research indicates that instruction-tuned models use these participial constructions at two to five times the rate found in human-written text.
This structural predictability extends to paragraph organization as well. AI tends to construct paragraphs with rigid consistency: a clear topic sentence, followed by supporting evidence, and concluded with a summary statement. While this structure represents sound writing practice in many contexts, the strict application creates text that feels overly neat and mechanical. Human writers naturally vary their paragraph construction based on purpose and rhythm, sometimes beginning with an anecdote, other times with a question, and occasionally launching directly into evidence without preamble. The absence of this variation in AI text creates what researchers describe as low “burstiness”, a lack of natural variation in sentence length and structure that characterizes human expression.
Formal and Academic Vocabulary Choices
The lexicon of AI-generated text reveals another layer of artificiality through its consistent selection of formal, slightly academic vocabulary. Words like “delve,” “underscore,” “harness,” “illuminate,” “facilitate,” and “bolster” appear with unusual frequency. These choices reflect the model’s training on vast amounts of professional and academic writing, where such vocabulary is standard. However, the overuse of these terms in contexts where simpler language would suffice creates prose that feels unnecessarily elevated.
This tendency extends to the selection of evocative but ultimately generic nouns. Terms like “tapestry,” “realm,” “beacon,” and “cacophony” appear regularly in AI text as attempts to add color to otherwise straightforward descriptions. Recent research has identified that different AI models even have distinct vocabulary preferences—what researchers call “aidiolects.” For instance, certain models favor words like “intricate” and “underscore,” while others show preference for “palpable” and “continuation.” These vocabulary fingerprints become increasingly obvious as readers encounter them across multiple pieces of AI-generated content.
The Absence of Genuine Personal Voice and Emotional Resonance
Ascending in our ranking, we encounter one of the more fundamental limitations of AI-generated text: the lack of authentic personal voice and emotional depth. The default tone of most language models is formal, neutral, and emotionally detached, often described as reading like corporate documentation or technical manuals. It is grammatically correct but lacking the subtle variations that convey personality. This flatness stems from the model’s fundamental nature as a statistical pattern matcher rather than a conscious entity with experiences and emotions.
The absence of lived experience manifests in subtle ways throughout AI text. Where human writers naturally infuse their prose with idiosyncratic preferences or emotional coloring that reflects their unique perspective, AI generates text that represents a statistical average of millions of voices. The instruction-tuning process that makes models helpful and harmless further flattens any stylistic variation, pushing output toward a generic, problem-solving persona. Even when prompted to adopt a specific tone or style, the underlying absence of genuine subjective experience creates a persistent emotional disconnect that careful readers can sense.
Excessive Use of Em-Dashes as Universal Punctuation
The em-dash has emerged as perhaps the most discussed punctuation-related indicator of AI generation. While perfectly legitimate in human writing, the em-dash appears with remarkable frequency in AI text—serving as a universal solution for connecting ideas, adding emphasis, or introducing explanations. This overreliance has become so pronounced that some observers have dubbed it the “ChatGPT dash,” a telling marker of machine generation.
The reason for this pattern lies in the model’s fundamental architecture. Language models lack an intuitive sense of rhythm or pause that human writers develop through years of reading and writing. Instead, they identify the em-dash as a versatile punctuation mark that appears across countless genres in their training data. It becomes a probabilistic shortcut—a way to create syntactic complexity without navigating the stricter rules governing semicolons, colons, or even commas. The em-dash serves as a Swiss Army knife of punctuation, deployed whenever the model needs to connect ideas but lacks the stylistic judgment to select more appropriate alternatives. While human writers might use em-dashes strategically for emphasis or dramatic effect, AI deploys them with mechanical regularity, transforming what should be a special-purpose tool into a default connector.