University professors still face the seemingly impossible task of detecting AI-fueled cheating, which continues unabated on both small and large assignments
RE: https://www.threads.net/... LinkedIn: Ian Bogost : As we enter the third year of AI college, I checked in on the AI cheating—and how universities are generally preparing. … Forums: Beehaw : AI Cheating Is Getting Worse
Context & Ripple Effects
The report lands after a Stanford survey that found no increase in high-school cheating rates in 2023, highlighting how uncertainty over prevalence did not remove the practical burden on instructors trying to judge individual work.
Later coverage makes the problem look less confined to isolated assignments: a UK survey recorded a sharp rise in proven AI-cheating cases, while students have also turned to text “humanizers” designed to evade detection.
First-order effects
- Professors must spend more time assessing whether submitted work reflects a student's own understanding, with no reliable way to resolve every doubtful case.
- Students' grades and disciplinary exposure become more dependent on inconsistent, instructor-level judgments when AI use cannot be clearly verified.
Second-order effects
- Universities face pressure to redesign assessment toward work that is harder to outsource and to clarify what AI assistance is permitted, rather than treating detection alone as a solution.
- An arms race between writing-generation, “humanizer,” and detection tools can make automated flags less decisive and increase the need for human review.
Third-order effects
- If detection remains unreliable, academic integrity may shift from policing finished text toward documenting process, supervised assessment, and explicit AI-use norms.
- The larger risk is educational rather than merely disciplinary: routine outsourcing can weaken the practice students need to develop independent competence, as coverage of AI's learning-process problem argues.
The trend: Education is moving from trying to identify AI-generated output to redesigning how learning and authorship are demonstrated in an AI-assisted environment.