[pinnate]

I’m a teacher that requires students to write, so I’m always on the lookout for reliable AI detection. Haven’t anything close to reliable yet, but Pangram definitely seemed to be better than anything else in my (very limited) tests.

That said, it did incorrectly flag something that I personally wrote as AI, which baffled me. I spent a while playing around with the text and feeding it back into Pangram, trying to figure out what the issue was. Turns out, I had a section in the text with three bullet points. I removed the bullets (literally just the bullets themselves, no actual text) and it passed as human. While it seems to perform better than anything else, I’m a bit skeptical of their claimed success rate.

[ActivePattern]

It's interesting that it cares about formatting choices like bullet points.

On the one hand, it makes the system more brittle and arguably overfit, since it's not making a decision on the content of the writing itself. Most people would say that AI writing is still AI writing even if bullet points are removed, and vice versa.

On the other hand, we know that AI writing does has a very specific formatting signature (e.g. em-dashes, bullet points) so it seems unwise to completely ignore it.

[MisterTea]

> It's interesting that it cares about formatting choices like bullet points.

Since AI I have noticed a lot of sudden bulleted lists appearing in pretty benign forum posts. That along with "You are right" "You are correct" "you're right" as the response.

[Aurornis]

The tools will never be perfectly reliable. It’s good that people like you are aware that it’s a statistical tool, not a perfect oracle.

For student work I would think it’s reasonable to look for patterns across each student’s work. A single Pangram positive across 10 assignments shouldn’t be enough to bring suspicion upon a student, but when 8 out of 10 of someone’s assignments are coming up as AI it’s time to talk.

The trend I’m hearing from my teacher friends is that students are generating AI papers and then putting them into tools like ZeroGPT and Pangram before submission. They do what you did: Remove sections, rewrite things, and make adjustments until the AI score goes down enough.

I heard that one classroom had developed a trick of adding spelling mistakes to ChatGPT output to try to make it look human written. So the teacher was getting pure AI slop but every paragraph had a couple typos inserted.

As for the 3 bullet points: That was a huge tell for one of the recent popular models. Almost all writing output would get sections of plus or minus 3 bullet points in a certain style. The newer frontier models do it less but that was a big LLM pattern for a while. Unfortunate for anyone who liked to write with 3 bullet points commonly. They’re feeling the pain of people who were emdash enjoyers before 2023

[NitpickLawyer]

> I would think it’s reasonable to look for patterns across each student’s work. A single Pangram

Strong disagree here. If a tool fails the basic test of teacher getting flagged on some text that they wrote personally, that tool should never be used. Being accused of cheating can have huge repercussions on a student's future. No way should a black-box classifier be used to decide their fate.

[Aurornis]

I said we shouldn't penalize students for single positives. That's bad use of statistics.

It's also bad use of statistics to claim that an indicator can never be used unless it's 100% perfect. When you start accumulating constant triggers, you do a deeper investigation.

For example, bring the student in and give them a surprise (no preparation) quiz about the paper they submitted. The students who wrote the paper can talk about what they wrote. The students who submitted ChatGPT usually can't.

You don't just put the paper into Pangram and then let it decide if the student gets punished based on the score.

[tzs]

> For student work I would think it’s reasonable to look for patterns across each student’s work. A single Pangram positive across 10 assignments shouldn’t be enough to bring suspicion upon a student, but when 8 out of 10 of someone’s assignments are coming up as AI it’s time to talk.

People mention Pangram has a low false positive rate, but usually such statistics are over a large population. They tell me that if I check many random works I should get a low number of false positives.

But in your example you aren't checking 10 random works from an assortment of authors. You are checking 10 works from one author.

The example from the teacher suggests that something as simple as a section with 3 bullet points can lead to flagging. When you are checking 10 works from one author all it might take for them to get most of them flagged even if they are 100% human written is for that author to have some style choice, like those 3 bullet point sections, that they just use in much of their writing.

This raises the question of what a teacher should do if they talk to the kid and the kid says it was all human written. Should they do what that teacher did when his own written was falsely flagged and start figuring out what tweaks would get it go pass?

That might help if by doing that you find out that all the 8 flagged items have one simple tweak that clears them up. Then I think you have to give the kid the benefit of the doubt and act is if their style just clashed with the detector.

But if different ones take different tweaks to fix that doesn't really say much, because it seems unlikely a detector would have just a single weakness (like the 3 bullet lists) that is good at false positives.

As I mentioned it is usually error rates on random populations that get quoted at least when I see articles about AI detectors. Have any of the leading checkers published research on the distribution of error rates when checkers are used on multiple works from a single individual?

[HelloMcFly]

I wrote a novella with epigraphs at the beginning of the chapters. Every epigraph was flagged as AI-generated likely because they were written as curt, clipped statements of fact as if taken from a historical record entry.