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Professor's invisible prompt trap catches 32/35 students cheating with AI

Professor's invisible prompt trap catches 32 students cheating on their midterm with AI

techspot.com

July 27, 2026

2 min read

🔥🔥🔥🔥🔥

48/100

Summary

A professor at Alcorn State University used a hidden white font in an assignment prompt to instruct AI to generate irrelevant responses. This method successfully identified 32 students who attempted to cheat on their midterm exam.

Key Takeaways

  • Dr. Jason Gibson, a history professor at Alcorn State University, caught 32 out of 35 students cheating on their midterm by embedding invisible instructions in the assignment prompt that directed AI to include nonsensical references to Madagascar.
  • Students who relied on AI for their answers failed the midterm portion due to the irrelevant content generated by the AI.
  • The use of AI for cheating in academic settings has become a widespread issue, with professors employing various strategies to combat it, including handwritten work and oral tests.
  • A professor at Brown University reported a significant increase in midterm averages after allowing take-home exams, suggesting that many students used AI to complete their assignments.
Read original article

Community Sentiment

Negative

Positives

  • The fact that AI can generate text that students pass off as their own shows just how advanced these models have become, highlighting their potential in education.
  • This incident underscores the need for innovation in teaching methods, potentially pushing educators to rethink their approaches in light of AI capabilities.

Concerns

  • Students are outsourcing their cognitive functions to AI, which raises serious concerns about their ability to think critically and engage with the material.
  • The prevalence of cheating with AI suggests a grim future for education, where students might prioritize short-term gains over genuine understanding.
  • Many commenters doubt the authenticity of the professor's claims, pointing to a possible bias in reporting on student cheating incidents.

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