A similarity score is easy to display. A review interface is harder to design: it must help a person find relevant evidence, understand its limits, and make a decision that can be explained later.
At Unplag.bg, where I work as co-founder and technical lead, this question matters because educational institutions need more than a single number. Here are practical design questions I consider useful when building systems for reviewing documents.
Keep the source close to the match
A highlighted passage should make it clear what was compared and where the corresponding source can be found. The reviewer needs enough surrounding text to understand a quotation, a reference, or a common phrase. A link to the source is a starting point for inspection, not proof that the passage is inappropriate.
Preserve the original and expose extraction problems
Document processing starts before comparison. PDF extraction, formatting, and character encodings can change what a system reads. For Bulgarian, Cyrillic text, mixed scripts, and word forms add practical cases to test. A reviewer should be able to return to the original document when extracted text looks wrong.
Separate different kinds of evidence
Text overlap and a signal about possible AI-generated writing answer different questions. Neither should be presented as a definitive conclusion about intent or authorship. Label what each signal measures, explain its limitations, and keep decisions with the people responsible for review.
Make the review understandable later
A useful workflow lets an authorised reviewer trace the relevant passages, sources and decisions. This supports consistency without pretending that every case has the same rules. Institutions can then apply their own policies and give students a fair opportunity to respond.
Designing for context is a product choice: it makes the limits of automation visible and gives educators a better basis for judgment.
Originally published on my Hashnode blog. Prepared with AI assistance. I am co-founder of Unplag.bg.
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