u/Existing_Process_151

From August 2026, the EU AI Act introduces new transparency requirements around AI-generated content.

What I find more interesting than the regulation itself is what happens when these signals reach universities.

For text, watermark detection is unlikely to work like a binary “AI / not AI” test. The signal can be probabilistic, can weaken after editing, translation or rewriting, and human-written text may potentially trigger it as well.

So imagine a professor runs a student's paper through a detector and gets a 17% probability that a watermark is present.

What exactly should happen next?

Nothing? A conversation with the student? Additional assessment? At what probability does it become evidence of academic misconduct?

There is another problem I think is getting less attention: students may start adapting their writing to detectors.

One student I interviewed described a group assignment where they kept checking and rewriting paragraphs until eventually they were no longer writing in their natural style. The detector had effectively become another audience for the essay.

That seems like a bad direction for assessment. Students should be optimizing for reasoning, evidence and clarity, not for whether an algorithm considers their prose sufficiently “human.”

It also risks collapsing very different uses of AI into one category. There is a meaningful difference between using AI to organize material or reduce routine work and outsourcing the actual reading, synthesis and reasoning.

I’m curious how people working in higher education would approach this. Should watermark signals ever be treated as evidence on their own, or only as a reason to examine the student's actual understanding?

Full article and interview context: https://canvas-assistant.com/blog/ai-watermarks-are-coming-what-will-universities-do-with-them

I wrote an article about how generative AI may be changing the learning process itself, based partly on interviews I conducted with students and a university lecturer.

What interested me most was the contradiction in their responses.

Students described genuinely valuable uses: accessibility support, explanations when teachers or parents are unavailable, practice questions, help structuring projects, and support with unfamiliar material.

But the same students also talked about thinking less, searching less, trusting AI too easily, and sometimes allowing it to do work they felt they should have done themselves.

One observation I did not explore in the article, because it was already getting rather long, was how reluctant some students were to talk openly about their AI use. My impression was that there was a degree of shame or discomfort around it.

Two things seemed to contribute to this. One was environmental guilt, which some students mentioned explicitly. The other was harder to define: an intuitive feeling that even when AI use did not clearly qualify as cheating, something about outsourcing too much of the cognitive work still felt wrong.

A lecturer I interviewed described another pattern: stronger, more reflective students can use AI to deepen their work, while students who have not yet developed independent academic habits may use it to substitute for that process.

The article asks whether AI should be understood not simply as another educational tool, but as a medium that increasingly participates in the cognitive process itself.

I’m the author, and I’m sharing it here because I’d be interested in how educators and students here are experiencing the same shift.

Where do you think the boundary lies between AI that supports learning and AI that substitutes for it?

Article: https://canvas-assistant.com/blog/the-medium-is-the-mind-what-ai-is-doing-to-education