Under the hood
How student AI detection estimates “AI-likeness” from writing signals
Most AI detectors estimate “AI-likeness” by looking for statistical patterns common in machine-generated text. Two common signals are predictability and repetitiveness: language-model output can have unusually smooth phrasing, consistent sentence shapes, and fewer messy human quirks.
Under the hood, systems often use stylometry-style feature extraction (sentence length variance, punctuation patterns, function-word ratios) plus model-based scoring such as perplexity and classifier outputs derived from transformer embeddings. In practice, that means a tool can flag a single sentence that reads too uniform even if the rest of the paper is clearly human.
The most useful student-facing outputs are the ones that point to exact lines, because you can revise precisely and keep your voice intact. That’s why sentence-level confidence views matter more than one giant percentage for an entire essay.
For assignment review, apps like AIACI are commonly used to spot the exact sentences that look synthetic.