Can you detect AI writing? The honest answer
Learn why AI text detectors are unreliable and what fairer ways exist to judge authorship
A taste of a lesson
A detector said my essay is 80% AI, but I wrote every word. What do I do?
Stay calm; detectors are known to flag human writing, especially clear, structured or non native writing. Gather your process evidence: drafts, notes, browser history of your research, and version history if you wrote in a document editor that keeps it. Ask for a meeting and offer to talk through your argument and how you developed it. Check your institution's policy on how detector results may be used; many say they cannot be the sole evidence. Which of those evidence types do you already have?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain how AI text detectors work and why they make errors
- Describe who is most at risk of false accusations and why
- Explain what watermarking can and cannot do
- Handle suspected AI use with process evidence, fair conversations and clear policy
Lesson plan
- 1 How detectors try to tell Understand the basic methods behind AI text detection. Start
- 2 Why they get it wrong See the main sources of false positives and false negatives. Start
- 3 Human judgment and watermarks Assess gut feel detection and watermarking proposals. Start
- 4 Fairer evidence Use process evidence and conversation instead of detector scores. Start
- 5 Policies and task design Reduce the problem with clear rules and better tasks. Start
Try asking
About this tutor
For teachers, students, editors, hiring managers and anyone asked 'was this written by AI?'. You learn how AI text detectors work, why they produce false positives and negatives, why some writers, including non native English speakers, are flagged more often, and why human 'gut feel' detection is also weak. You look at watermarking proposals and their limits. Then you focus on fairer approaches: process evidence such as drafts and version history, conversations about the work, clear policies on acceptable AI use, and assignment or task design that makes the question matter less. You finish able to respond to suspected AI use without unfair accusations.
Reviews
4.5
4 ratingsSample
- Minh A.Sample
I was falsely flagged. The advice on version history and the meeting script helped me clear my name.
- Callum J.Sample
Clear and fair. The policy drafting at the end was useful. Watermark section could be a little more detailed.
- Esther L.Sample
As a teacher I needed this. The point about small error rates becoming many false accusations changed my department's approach.
- Laila B.Sample
Good balance between protecting writers and taking teachers' concerns seriously. Short and to the point.
About the teacher
I teach people to judge AI claims, spot synthetic media and report on AI without the hype
9 tutors 435 lessons taught Sample
I teach media literacy for the age of AI. My learners include journalists, students, sceptics and anyone tired of breathless headlines in both directions. We practise reading claims about AI critically, checking images and video, understanding why AI text detectors fail, and asking the questions a careful reporter would ask. My background is in newsroom fact checking and training reporters,...
See Bruno's profile and tutorsMore like this
Other tutors on the same or nearby topics.