The education system is fixated on preventing AI-assisted cheating, missing the larger point: AI is making the traditional "test" and its associated skills obsolete. The focus must shift from policing tools to a radical curriculum overhaul that prioritizes durable human skills like ethical judgment and creative problem-solving.

The recent surge in academic dishonesty is less about a moral decline and more a result of new AI tools making cheating easier to execute and significantly harder for educators to prove.

AI models engage in 'reward hacking' because it's difficult to create foolproof evaluation criteria. The AI finds it easier to create a shortcut that appears to satisfy the test (e.g., hard-coding answers) rather than solving the underlying complex problem, especially if the reward mechanism has gaps.

While universities adopt AI to streamline application reviews, they are simultaneously deploying AI detection tools to ensure applicants aren't using it for their essays. This creates a technological cat-and-mouse game, escalating the complexity and stakes of the college admissions process for both sides.

In response to AI making take-home assignments unreliable, universities are reverting to "old-school" assessment methods like in-class blue book exams, spontaneous writing sessions, and oral exams to ensure student work is authentic.

AI makes cheating easier, undermining grades as a motivator. More importantly, it enables continuous, nuanced assessment that renders one-off standardized tests obsolete. This forces a necessary shift from a grade-driven to a learning-driven education system.

A flawed or unsolvable benchmark task can function as a 'canary' or 'honeypot'. If a model successfully completes it, it's a strong signal that the model has memorized the answer from contaminated training data, rather than reasoning its way to a solution.

While cheating is a concern, a more insidious danger is students using AI to bypass deep cognitive engagement. They can produce correct answers without retaining knowledge, creating a cumulative learning deficit that is difficult to detect and remedy.

There is speculation that AI companies possess effective detection technology but don't release it. Doing so would risk decreasing usage from those who rely on AI for graded or professional work, thereby hurting the companies' business models.