From Copy & Paste to AI-Generated Texts
Just a few years ago, the fear of plagiarism was omnipresent. Universities invested large sums in plagiarism detection software, and students dreaded the percentage scores in examination reports. The rule was clear: anyone who copied text passages word-for-word risked being accused of cheating.
But the academic landscape is changing rapidly. AI text generators such as ChatGPT, Jasper, or Claude can now produce entire essays at the push of a button — often in fluent, freshly phrased language, but without clear sources. This turns the old logic upside down:
- Classic plagiarism is becoming rarer.
- Invisible lack of sources is becoming the real problem.
So the question is: Do we still need traditional plagiarism detection in the age of AI — or do we need to adjust our understanding of “academic integrity”?
What is plagiarism detection software and how does it work?
Plagiarism software pursues a clear goal: detecting external texts within submitted work. It does this through different methods:
Database comparison
- Compares with online sources, libraries, earlier papers.
- Detects word-for-word identical passages.
String-matching & pattern recognition
- Searches for recurring word sequences.
- Also works with small variations (synonyms, rearranged sentences).
Quota analysis
- Software generates a report with “X% match.”
- Often flags seemingly harmless results (e.g., standard phrases).
In the pre-AI era, this made sense: copy & paste from Wikipedia or other students’ essays could be reliably detected.
Why classical plagiarism detection is losing relevance in the AI age
Today the situation looks different. AI systems are generative models: they produce text anew instead of copying it word-for-word. This has three consequences:
- No more identical text passages
A paragraph written by ChatGPT about “globalization” will never match an existing article word-for-word.
Traditional plagiarism software finds nothing — even though the AI’s content may be heavily based on existing sources. - Apparent “originality”
Plagiarism reports often show a 0% match. For lecturers, this looks positive at first. In reality, it’s a false sense of security! - A new form of “academic misconduct”
The problem is shifting:- Earlier: Copy & paste without citation.
- Today: AI-generated text without proper sources.
This is not classical plagiarism, but it is still incompatible with academic work.
Why correct citation is more important than plagiarism software
- Traceability
Science thrives on transparency. Every reader must be able to see where an idea came from. - Verifiability
Without sources, claims cannot be verified. Especially in the AI era, there is a danger of hallucinations — the AI “invents” facts. - Credibility
Papers with complete reference lists appear serious and trustworthy. Missing citations immediately raise doubts.
What universities and students need now
New examination methods
- Don’t just look at the “plagiarism percentage.”
- Instead, evaluate the quality of citations.
- Tools like Citalyze help automate cross-checking between in-text citations and the bibliography.
Setting the right priorities
- Less focus on “percentages.”
- More focus on: “Are all ideas properly supported?”
Responsibility of students
- Anyone using AI must be transparent about it.
- Proper citations remain mandatory — even if passages were suggested by AI.
Conclusion: From plagiarism detection to source verification
Plagiarism detection alone is no longer sufficient. AI has changed the playing field:
- Classical “copy & paste” plagiarism is disappearing.
- The real risk lies in papers without verifiable sources.
Therefore:
- Proper citation is the new “plagiarism avoidance.”
- A clean bibliography is more important than any percentage score in a plagiarism report.
Universities, lecturers, and students must shift their focus — away from pure plagiarism detection and toward citation and source competence.