Plagiarism control in the age of AI. Why WISEflow Originality takes a stand
Plagiarism detection is no longer a simple matter of matching strings of text. As universities grapple with the dual challenges of AI-generated content and ever-more sophisticated paraphrasing, the tools and policies we choose matter more than ever. Yet, in the rush to “do something”, it is easy to reach for solutions that are outdated, unreliable, or even counterproductive. At WISEflow, we believe in doing better. Here is why.
Whether students should have access to plagiarism checks before submission is a double-edged sword
It sounds reasonable. Let students check their work for plagiarism before submitting. After all, who would not want to fix a careless mistake or learn from a similarity report? Some universities have embraced this approach, arguing it builds trust, transparency, and academic skills.
But reality is more complicated. After careful consideration, several project groups have recommended against student access to plagiarism control tools. Why? Because the reports are not always easy to interpret, especially for inexperienced users. Without robust support and training, students risk misunderstanding the results, potentially leading to false confidence or unnecessary anxiety. Worse, there is a risk that some might use the system to “game” the checker, iteratively editing their work to evade detection rather than learning genuine academic integrity.
At WISEflow, we share these concerns. Our priority is to ensure that plagiarism control is a meaningful part of the assessment process and not a box-ticking exercise or a tool for strategic evasion. Until we can guarantee that student-facing reports are truly educational and that institutions are equipped to support their use, we believe it isresponsible not to offer this feature.
The unreliable and risky mirage of AI detection
With the explosion of generative AI, the temptation to deploy AI detectors is strong. But let us be clear. Current AI detection tools are, at best, unreliable. Project groups have found that such detectors often produce false positives, lack of transparency, and cannot be meaningfully audited or explained. This is not just a technical quibble;false accusations of misconduct can have serious consequences for students.
Recent research echoes these concerns. For example, a 2023 study in the International Journal for Educational Integrity found that AI detectors frequently misclassify both human and AI-generated text, especially when the writing is non-native or highly paraphrased (Joshi, 2023). The lack of insight into how these models reach their conclusions makes them unsuitable as evidence in academic misconduct cases.
That is why WISEflow Originality does not offer AI detection. We refuse to compromise on fairness and transparency. Until the science catches up and it may never do so reliably. We will not risk students’ academic futures in a black box.
Why translation-based plagiarism checks are obsolete and semantic analysis is the future
Some plagiarism systems still rely on translating texts into a handful of major languages and then comparing them for similarity. This approach is, frankly, a relic. It is cumbersome, error-prone, and easily defeated by even modest paraphrasing. Testing has shown that such translation tools only catch the most trivial cases, those that a human marker would likely spot unaided. The cost is high, the benefit marginal, and the risk of missing sophisticated plagiarism is real.
Semantic analysis, by contrast, is the future. Instead of looking for literal matches or crude translations, semantic models (like those used in WISEflow Originality) understand meaning and can detect paraphrasing and conceptual similarity. This is not just marketing hype, recent advances in natural language processing have made it possible to identify when ideas, not just words, have been copied or reworked without proper attribution (see Alzahrani et al., 2012, ACM Computing Surveys).
By focusing on genuine semantic similarity, WISEflow Originality offers a more robust, fair, and future-proof approach to plagiarism detection. It is not about catching students out. It is about upholding academic standards in a world where copying is easier than ever.
The conclusion is integrity over optics
It is tempting to offer every feature under the sun, to reassure stakeholders that “something is being done”. But at WISEflow, we believe that integrity, of both process and technology, matters more. That is why we do not offer unreliable AI detection, why we do not give students access to confusing reports, and why we have moved beyond translation-based checks to true semantic analysis.
Academic integrity deserves more than quick fixes. It deserves tools and policies that are as rigorous and thoughtful as the scholarship they protect.
Take action by choosing control, privacy, and clarity with WISEflow Originality
f your institution is ready to move beyond outdated approaches and embrace a more responsible solution, we would gladly talk about WISEflow Originality and our approach. In such conversation we could also tell more about how we provide advanced sharing options and put institutions in control, allowing you to decide exactly who can access data, documents and resources, ensuring higher compliance with privacy and GDPR requirements. Moreover, we could demo our embedded, user-friendly reporting which makes it easy for educators to interpret results and take informed action without confusion.
Let’s raise the bar for academic integrity together. Contact us to learn more about how WISEflow Originality can support your institution’s needs with transparency, security, and genuine innovation at the core.
References
Joshi, S. G. (2023). “Artificial Intelligence Detectors: Reliability and Risks.” International Journal for Educational Integrity.
Alzahrani, S. M., Salim, N., & Abraham, A. (2012). “Understanding plagiarism linguistic patterns, textual features, and detection methods.” ACM Computing Surveys, 44(2), 1-38.
Project group recommendations, November 2025.