Educators Should Think Like Hackers (kelvinpaschal.com)

A recent essay framed the LLM-driven cheating dilemma through concrete examples—like the suspended Columbia student’s “interview coder” tool that helps people answer LeetCode-style questions for FAANG interviews—and argued that widespread use of LLMs for essays, homework and quizzes has provoked panic among educators. The author traces the problem to incentives: degrees and GPAs are treated as targets, so students rationally optimize for those metrics (Goodhart’s law), using AI as a near-instant shortcut. Rather than blaming students, the piece reframes them as “black hat” attackers who exploit rigid evaluation systems. The proposed remedy is to adopt a security/hacker mindset: become “white hat” educators who probe vulnerabilities in assessment design and iterate defenses. Practically, that means moving away from easily automated, output-focused tests toward assessments that value process and uniqueness—oral presentations, live Q&A, multi-stage projects, and metrics that reward planning, collaboration and critical thinking. For the AI/ML community this signals two implications: detection arms races are limited, and durable solutions lie in redesigning evaluation and pedagogy to test skills LLMs can’t fully replicate (interactive reasoning, real-time problem solving, and reproducible project work). Continuous revision of methods, not just prohibition, is the advocated path forward.