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Over the course of a year, I may get almost 500 assignments. If there is no reasonable way to verify if a submission flagged by a tool actually is AI-assisted o

Over the course of a year, I may get almost 500 assignments. If there is no reasonable way to verify if a submission flagged by a tool actually is AI-assisted or not (and IMHO there isn't), then even a 99% accurate tool is useless - I can't simply make 5 high-impact false accusations of innocent students each year, so these 'detections' are not actionable.

inimino 4y ago
Or you could wait until you have 5 flagged submissions from the same student, and then, I don't know, apply human scrutiny and your own good judgment?
mcv 4y ago
You could ask the the flagged students questions about what they wrote.
YeGoblynQueenne 4y ago
Isn't it the same thing with plagiarism detection tools? Don't they also have false positives, too high to act on them? I don't know how that works in practice (I don't teach, though I've TA'd and noticed how there were clusters of assignments that were similar not only to each other, but also to the previous years' ones). So I'm curious what's the done thing. Does a tutor just confront a student with the "evidence" in the form of the plagiarism detector's score, say, and wait for the student to confess? How, exactly, can you prove not only whom, but how, wrote an assignment? I mean in the general case?
- PeterisP 4y ago
  No, it's not the same thing because for plagiarism detection tools we don't really base a decision on the tool's output but rather compare the submitted document with the supposedly plagiarized original that the tool identified, and in those cases both verbatim copying without attribution and trivial changes can be identified by a human, the tool is just generating suggestions for human review. On the other hand, if a tool thinks that the text has an x% probability[1] of being AI-generated text, I have nothing to base a decision other than the output of that tool - if I'm not willing to fully delegate the decision to that tool (and I'm not) then I have no practical means to verify if the tool's suspicion is right. [1] And as someone working on machine learning, let me spare you a lecture on that a ML classifier saying "x%" can not be safely interpreted as x% probability, there's a million caveats that the vast majority of papers and tools don't try to address.
  - YeGoblynQueenne 4y ago
    Thanks for the clarification!

inimino 4y ago

Or you could wait until you have 5 flagged submissions from the same student, and then, I don't know, apply human scrutiny and your own good judgment?

mcv 4y ago

You could ask the the flagged students questions about what they wrote.

YeGoblynQueenne 4y ago

Isn't it the same thing with plagiarism detection tools? Don't they also have false positives, too high to act on them? I don't know how that works in practice (I don't teach, though I've TA'd and noticed how there were clusters of assignments that were similar not only to each other, but also to the previous years' ones). So I'm curious what's the done thing. Does a tutor just confront a student with the "evidence" in the form of the plagiarism detector's score, say, and wait for the student to confess? How, exactly, can you prove not only whom, but how, wrote an assignment? I mean in the general case?

PeterisP 4y ago
No, it's not the same thing because for plagiarism detection tools we don't really base a decision on the tool's output but rather compare the submitted document with the supposedly plagiarized original that the tool identified, and in those cases both verbatim copying without attribution and trivial changes can be identified by a human, the tool is just generating suggestions for human review. On the other hand, if a tool thinks that the text has an x% probability[1] of being AI-generated text, I have nothing to base a decision other than the output of that tool - if I'm not willing to fully delegate the decision to that tool (and I'm not) then I have no practical means to verify if the tool's suspicion is right. [1] And as someone working on machine learning, let me spare you a lecture on that a ML classifier saying "x%" can not be safely interpreted as x% probability, there's a million caveats that the vast majority of papers and tools don't try to address.
- YeGoblynQueenne 4y ago
  Thanks for the clarification!