Definition
Sentence-Level Detection assigns AI probability scores to individual sentences rather than producing a single score for the entire document. This approach identifies exactly which parts of a text are likely AI-generated, making it far more useful for educators reviewing mixed or partially AI-assiste
Most early AI detectors produced a single overall score for a document – a percentage indicating how likely the entire text was to have been AI-generated. This document-level approach has significant limitations: it cannot locate which specific passages triggered the flag, it is unreliable on short documents, and it provides insufficient detail for defensible academic integrity decisions.
Sentence-level detection addresses these limitations by breaking the analysis down to the smallest meaningful unit of text. Each sentence is scored independently, allowing the detector to build a granular map of the document’s AI probability across the full text.
How It Works
In sentence-level detection, each sentence is passed through the detection model with its surrounding context. The model assigns an AI probability score to each sentence based on signals including token probability, perplexity, and burstiness – measured locally within that sentence and its context window. The scores are displayed inline with the text, typically using color-coding to indicate AI probability levels from low to high.
This approach is more computationally intensive than document-level scoring but produces significantly more actionable results. It can identify specific paragraphs that were AI-generated within otherwise human-written work, flag transition points between AI and human writing, and reveal editing patterns consistent with AI use.
Why It Matters for AI Detection
Sentence-level detection is the standard Proofademic was built around because it is the most useful format for the actual decisions educators need to make. A document-level score of 75% tells an educator very little – it could mean the entire document is moderately AI-influenced, or that a few paragraphs are heavily AI-generated within an otherwise human-written submission.
Sentence-level scores change that entirely. An educator can see exactly which passages are flagged, evaluate whether those passages make sense in context, and have a more specific, evidence-based conversation with the student about the flagged sections.
FAQs
Is sentence-level detection more accurate than document-level detection?
It provides more granular information rather than simply being more accurate. For mixed documents – where some sections are human-written and others are AI-generated – sentence-level detection is substantially more useful because it identifies the specific passages of concern rather than averaging across the whole document.
Does Proofademic use sentence-level detection?
Yes. Sentence-level analysis is a core feature of Proofademic, allowing educators to see which specific sentences contributed to the overall detection score. This makes assessment decisions more informed and defensible compared to tools that only provide a single document-level percentage.
How does sentence-level detection handle very short sentences?
Very short sentences (fewer than 5-6 words) provide minimal statistical signal and are typically assessed with lower confidence. Most sentence-level detectors assign these sentences a score but weight them less heavily in the overall analysis, or display them with a lower confidence indicator alongside the probability score.
Can sentence-level detection identify where human editing was applied to AI text?
Yes – this is one of its most valuable capabilities. When a student generates AI text and manually edits or rewrites specific sections, those human-edited passages often display lower AI probability scores than the surrounding AI-generated content. This creates a detectable pattern: lower AI probability scores within a document that otherwise scores high overall.