Checking coursework for AI-generated writing before you submit it
Academic integrity offices have caught up to generative AI faster than most students expect, and the range of policies is wide: some departments ban AI assistance outright, some allow it for brainstorming but not drafting, and some require an explicit disclosure statement whenever AI touched a submission at all. Whatever your instructor's policy is, the safest position is knowing what your own writing looks like to an AI detector before you turn it in — not to game the system, but to catch the difference between "I edited this heavily" and "this still reads like a first AI draft."
This checker runs your text through detection analysis and reports which sections read as likely AI-generated, likely human-written, or mixed. It's built to be useful across every kind of assignment — essays, lab reports, discussion posts, problem set write-ups — regardless of which AI tool, if any, was involved in drafting them.
What a detector score does and doesn't tell you
A detector estimates the statistical likelihood that a passage was machine-generated, based on patterns like predictability of word choice and uniformity of sentence structure. That's a meaningfully different thing from a verdict on academic honesty. Detectors can and do misfire in both directions:
- False positives on human writing. Non-native English speakers, writers with a formal or simplified style, and even careful technical writers can get flagged, because their sentence patterns happen to be more predictable than average.
- False negatives on AI writing. Heavily edited AI drafts, or AI output run through a paraphraser, can score as human even when the original ideas and structure came from a model.
- Disagreement between tools. Different detectors are trained differently and routinely disagree on the same passage — there's no single ground truth score.
Treat a detector result as a signal to investigate, not a conclusion to act on unilaterally — and understand that most institutions using AI detection as evidence in an integrity case are supposed to weigh it the same way.
Reading your policy before you read your score
Before running anything through a checker, it's worth actually locating your course's or institution's AI policy — usually in the syllabus, the academic integrity handbook, or an LMS announcement. Policies generally fall into a few buckets:
- Full prohibition: No AI assistance at any stage, often enforced with detector spot-checks and process evidence (draft history, version timestamps).
- Permitted with disclosure: AI tools are allowed for specific stages (outlining, editing) if you say so in a statement or citation.
- Unrestricted for learning support: Some courses actively encourage AI use as a study aid, with the expectation that submitted work is still your own synthesis.
A detector score means something different in each of these contexts. In a full-prohibition course, any AI-pattern flag is worth resolving before submission. In a disclosure-based course, the flag matters less than whether you disclosed accurately.
Using this checker responsibly
- Paste the section you're unsure about — a full paper or just the parts you drafted quickly.
- Read the flagged sections and ask honestly whether they reflect your own analysis or a first AI pass you didn't fully rework.
- For flagged passages you wrote yourself, don't panic — revise for your natural voice rather than trying to "beat" the detector artificially.
- For passages you know came from AI assistance, either rewrite them substantially in your own words or disclose the assistance per your course policy.
- Keep your drafts and notes. If a false positive ever gets raised, process evidence is usually more persuasive than arguing about detector accuracy.
For rewriting passages that need genuine reworking rather than just checking, see the AI Academic Humanizer. For institutional-grade checking against a specific detection service, see the AI Copyleaks Checker.
What integrity offices actually look at besides a detector score
Because detector scores alone are unreliable, most academic integrity processes that take AI use seriously build in additional evidence rather than treating a single number as decisive. It helps to know what that evidence usually includes, since it also gives you a sense of how to protect yourself if you did the work honestly:
- Version history. Google Docs, Word, and most LMS platforms keep edit history. A document that appears fully formed in one paste, with no incremental drafting, looks different from one built up over multiple sessions.
- Consistency with prior work. A sudden, dramatic shift in vocabulary or sentence complexity compared to a student's earlier submissions can be a signal independent of any detector.
- Ability to discuss the content. Some instructors will simply ask a student to explain or expand on a specific claim in their own submission — a reasonable check that a detector score can't replace.
- Citation and source accuracy. Fabricated or misattributed sources are a stronger, more concrete signal than writing style, and one this checker can't evaluate at all.
None of this is meant to be intimidating — it's meant to explain why keeping your drafts, notes, and outlines is more protective than obsessing over a single detector percentage.
Common misconceptions worth clearing up
- "A 0% AI score proves I wrote it myself." It doesn't — it means the detector didn't find the patterns it looks for, which is a different, narrower claim.
- "Any AI assistance means an automatic violation." Only true if your specific policy says so. Many courses distinguish between using AI to brainstorm and using it to generate submitted text.
- "Detectors get more accurate over time, so old flags don't matter." Detector accuracy is inconsistent across updates and doesn't follow a clean upward trend — don't assume a past flag or a past clean result still applies to new text.
A note on group projects and collaborative writing
Group assignments add a layer of complexity a detector can't account for. If one teammate drafts a section using AI assistance and another rewrites it heavily, the finished paragraph reflects a mix of processes that no single score can meaningfully describe. Before submitting joint work, it's worth having a short conversation as a group about how each person used AI tools, if at all, so everyone understands what's actually in the final document and no one is caught off guard if a section gets flagged. This also protects the group if a professor asks about a specific passage — a shared understanding of how each part was written is more useful in that moment than any detector result.