05/06/2026
The more AI becomes entrenched in education, the more it disrupts the old systems we have relied on for decades.
Today, I want to focus on one of the biggest areas of disruption: plagiarism and academic misconduct.
I recently revisited a paper by Cecilia Chan (2023), where she introduces the concept of AI-giarism.
Chan defines AI-giarism as “an emergent form of academic dishonesty involving AI and plagiarism” (p. 1).
The term itself is interesting, but I think the deeper value of the paper lies in the conversation it opens.
For a long time, plagiarism was mostly understood as using another person’s words or ideas without proper acknowledgment. That definition still matters, of course, but it does not fully capture what is happening with generative AI.
AI does not simply give students someone else’s paragraph to copy. It can help them brainstorm, outline, rewrite, summarize, translate, polish, generate examples, structure arguments, and sometimes produce entire pieces of work.
Are these forms of AI use considered plagiarism in the 'old' academic sense?
This is indeed problematic and it forces us to reconceptualize the entire concept of plagiarism and cheating.
Copy/paste content whether from AI or any other medium is definitely an integrity issue, that's out of the question.
But AI-mediated practices do not always involve these kind of acts. People use it (including me) in more productive ways to help them build their ideas, express them in better ways, and create original contributions.
With this in mind, I think what we need is a different angle and a different set of questions:
What kind of assistance did the student receive?
At what point does assistance become collaboration?
When does collaboration blur authorship?
When does authorship become delegation?
And when does delegation become proxy performance, where the tool performs the intellectual work on behalf of the student?
These, I believe, are the new boundaries we need to define in the AI age.
Chan’s work provides an important starting point for this discussion. It also connects with a growing body of scholarship that invites us to rethink cheating, assessment, and integrity in the age of AI.
I am thinking here of work such as:
Eaton, S. E. (2023). Postplagiarism: Transdisciplinary ethics and integrity in the age of artificial intelligence and neurotechnology.
Corbin et al. (2025). ‘Where’s the line? It’s an absurd line’: Towards a framework for acceptable uses of AI in assessment.
Dawson et al. (2024). Validity matters more than cheating.
Nieminen, J. & Eaton, S. E. (2024). Are assessment accommodations cheating?
All of these point to the same larger shift: The AI age does not make academic integrity less important. It makes it more complex, more urgent, and more central to the future of teaching and learning.
