What it actually does
SafeAssign is Blackboard’s built-in plagiarism checker, owned by Anthology. It runs what its documentation calls “a unique text matching algorithm capable of detecting exact and inexact matching” between your submission and an existing corpus: academic papers, publications, articles and internet sources. It returns a similarity percentage and highlights the passages that matched, with the sources beside them.
That design is the whole answer to the AI question. Anthology’s own description of the model is blunt: the traditional anti-plagiarism approach “is based on building a large database of existing texts to compare with the learner’s submission, rather than on the ability to protect against texts created by generative AI.” A language model writes text that exists nowhere else. There is nothing to match.
The test Anthology ran, and published
Most vendors in this category publish research showing their product works. Anthology published research concluding that a product it was considering does not, and then did not ship it. The white paper says so directly: “both Anthology and participating clients concluded that AI detection is not currently fit for purpose in education.”
The test ran across May and June 2023 as part of evaluating whether to put AI detection inside SafeAssign. Sixty-five client institutions submitted more than 1,000 texts — some authentic, some AI-generated — then answered how accurately the detector had assessed them. Asked how often the tool correctly identified whether text was written by AI:
Anthology’s summary: 80% of respondents felt detectors were, at best, only able to “sometimes” identify texts correctly. The written feedback is sharper than the chart. One participant: “I used ChatGPT to generate five different texts. Four introduction paragraphs and two entire essays, and the AI detector completely misidentified all of them as being human generated.” Another: “It is easy to manipulate the tool into thinking the text was written entirely by humans.”
One caveat we would apply to our own evidence, so we will apply it here: this is a survey of what institutions concluded, not a controlled measurement. It records perceived accuracy after a hands-on trial, which is a real and useful signal about whether practitioners will trust a tool — but it is not the same kind of evidence as a peer-reviewed error rate, and it should not be quoted as one.
What this does not mean
Two mistakes are easy here, and both appear on pages currently ranking for this question.
It does not mean your Blackboard course has no AI detection. An institution can run a third-party detector alongside SafeAssign, through the LMS or outside it. SafeAssign having no AI detection is a fact about SafeAssign, not about your university.
It does not mean AI text will never be flagged by SafeAssign. It can be — for similarity. If a model reproduces phrasing that exists online, that overlap matches exactly like any other overlap. The score still means “this wording appears elsewhere”, which is a different accusation with a different defence.
Where your paper goes
Two separate stores, and the distinction matters. Your institution’s own database receives submissions by default. The Global Reference Database is separate and voluntary — papers there are checked against submissions from other institutions, which is what makes it useful and also what makes it permanent.
The commitment is stated plainly in Anthology’s student guidance: “When you submit your papers to the database voluntarily, you agree not to delete papers in the future.” To Anthology’s credit the same page also says “Blackboard LMS doesn’t claim ownership of submitted papers,” and that you are free to check a paper without contributing it.
The practical trap is timing: exclusion has to be set before submission. Work already submitted cannot be pulled back out afterwards. If you intend to publish a dissertation later, that is worth knowing on the day you upload it rather than the day you submit it to a journal.
What Anthology recommends instead
Its guidance to administrators points away from detection entirely and toward assessment design: tasks built on critical thinking, personal perspective and self-reflection; peer assessment and collaboration; and personalised assessments that let an instructor become familiar with how a particular student writes.
We think that is right, and it is worth noticing who is saying it. Anthology sells to the institutions that would have bought this feature. Declining to sell it, and publishing the reasoning, is a more credible signal than any vendor accuracy claim on this site or anyone else’s.
Where we stand
We sell a detector and a humanizer, so we have an obvious interest in how this question is answered, and you should weigh the page accordingly. We have published no measured accuracy figure for our own detector, and will not before our benchmark produces one under a method published in advance.
The honest summary for a student: a SafeAssign score is not an AI accusation, and if it is being treated as one, that is a factual error you can correct with the vendor’s own documentation. If a separate AI detector has been run on your work, that is a different conversation — start with what to do if you have been accused and how to prove you wrote it.
Compared against
- SafeAssign vs Copyleaks — SafeAssign against Copyleaks on what independent testing found, who each is sold to, and what happens to the text you submit.
Related
- SafeAssign vs Turnitin — the full side-by-side.
- Turnitin, reviewed — the tool that did ship AI detection
- Copyleaks, reviewed
- Are AI detectors accurate?