On 22 June 2026, Swiss public broadcaster SRF published a story with a headline that quietly captures the whole problem of AI in higher education: students may be cheating with AI — but almost nobody can prove it.
The numbers reporters Fiona Zellweger and Pascal Albisser gathered are strikingly small. PH Luzern has recorded 27 cases since the start of 2023. ZHAW counts 17 in the past two years. ETH reports only a low single-digit number per year. In an era where every student has a free chatbot in their browser, those figures are not a sign that cheating is rare. They are a sign that it is barely being measured.
The detection problem
At least 11 of the 31 Swiss universities surveyed use AI detectors — and they add the same caveat: the tools are not reliable. An AI detector does not produce evidence. It produces a probability, an “AI-likelihood” score that can flag a careful human writer as a machine and wave a machine-polished essay through as human. You cannot fail a student, or clear one, on a guess.
So universities are stepping back from the detector arms race. Berner Fachhochschule (BFH) deliberately does not count AI cases at all, treating AI use as a “question of competence” rather than a disciplinary offence. The University of St. Gallen (HSG) frames it as principle: “The use of AI should support learning, not replace it. The decisive element remains the student’s own academic work.” The University of Lausanne (UNIL) puts it bluntly: it is “fairly illusory to complete an entire degree programme by delegating everything to AI.”
The emerging consensus is transparency obligations, oral examinations, and trust — not technical surveillance. That is sensible. But it leaves a gap. If you can’t prove intent, what can you check?
Flip the question: don’t prove intent, verify the citations
Here is the move. Stop trying to prove how a text was written. Check whether what it says is true to its sources.
This is fundamentally different from detection, because it is objective. A claim either is or is not supported by the source it cites. A reference either exists or it does not. There is no “73% likely” — there is checkable evidence. And crucially, this question is fair to everyone in the room. It does not accuse anyone of misconduct. It simply asks the question every examiner asks anyway: does the source actually say what the citation claims?
That reframing also protects the honest researcher — the person AI detectors punish most. If you used an AI assistant to help organise your literature and it quietly handed you a citation that does not exist, or one that points to a real paper which never made the argument you attributed to it, you are now carrying a defect you never intended and may not even know about. A detector would only tell you your prose “looks AI-written”. It would never find the actual error. Verification does.
The two kinds of AI citation damage
When AI touches a bibliography, the damage comes in two distinct shapes:
- The fabricated source. Author, title, or DOI is simply invented. The paper does not exist. This is the famous “hallucinated reference.”
- The real source that doesn’t support the claim. The paper exists and is correctly cited — but it never made the point you attribute to it, or you cited the wrong page. This is subtler, more common, and far harder to spot by eye.
These need two different layers of checking — and there is a free tool for each.
Layer one: does the reference exist? — citecheck
citecheck is an open-source npm package by Tobias Lüscher. Point it at your bibliography (.bib, .ris, CSL-JSON, or extracted from .docx/.txt/.md) and it confirms each reference actually exists against Crossref and OpenAlex, flags retracted papers, and identifies open-access versions via DOAJ.
npx citecheck <file>No API keys, no signup. It runs locally; only reference metadata is sent to public scholarly APIs. It solves the first half of the problem cleanly: fabricated, non-existent, and retracted references.
Layer two: does the source support the claim? — Acurio
The second, subtler half is what Acurio was built for. Export your thesis as a DOCX with embedded Zotero citations, upload your source PDFs (or BibTeX/RIS), and Acurio reads each cited source and judges, citation by citation, whether it actually backs your claim. You get a colour-coded report:
- Supported / partially supported / unsupported for every citation
- A confidence score
- A verbatim quote from the source
- A short rationale
DOCX in, DOCX out. Multiple language models analyse each citation independently; when they disagree, a dedicated judge resolves the conflict in a single pass. Swiss data handling, FADP/DSGVO-compliant.
Acurio is not a plagiarism checker and not a style editor. Plagiarism tools find text too close to a source. Acurio finds the opposite failure — claims too far from the source, where the document does not say what you wrote. (More on that distinction in AI proofreading vs. citation checking.)
Together, the two are complementary layers: citecheck guarantees the reference is real; Acurio guarantees it earns its place in your argument. citecheck also powers Acurio’s free Quick-Check.
Limits — honestly
This approach is evidence-based, not magic. Acurio checks whether a cited source supports a claim; it cannot judge an argument you made without citing anything, and a verdict is only as good as the source PDF you upload. citecheck verifies that references exist and their status — it does not read the full text or assess argument quality. And none of this measures “did a human write this.” That, deliberately, is not the question we think is worth asking. The question worth asking is whether the work stands up to its sources — and that one, unlike intent, is genuinely checkable.
Where to start
- Try the free Quick-Check — paste a bibliography and see existence and status checks in seconds: app.acurio.ch/quick-check
- Run citecheck yourself —
npx citecheck <file>, open-source: github.com/tobiasosDev/citecheck - Check a full thesis — the first run is free; one-time Thesis packages from CHF 19, no subscription: see pricing
Students at ETH Zürich, LMU München, IU Internationale Hochschule, and Berner Fachhochschule already use it.
Want more background? Read how AI hallucinates sources, how to spot predatory journals, or our comparison of thesis-checking tools.
The detector debate asks an unanswerable question. The better question is older and simpler — does your source say what you claim it does? That one has an answer.
Sources
- SRF News, “KI an Hochschulen – Schummeln Studierende mit KI? Kaum jemand kann es belegen”, Fiona Zellweger and Pascal Albisser, 22 June 2026: srf.ch