What Happens After A.I. Destroys College Writing?

Assessment, Cheating, and Exam Design

• Many argue that “every undergrad study” can now be passed with AI, exposing how dependent systems are on take‑home writing and homework.
• Proposed fixes include: in‑class writing sprints, proctored/locked‑down computers, handwritten exams, oral/viva voce exams, and local test centers for remote programs.
• Others suggest tracking document edit history (e.g., Google Docs, timestamped proof‑of‑work), but this is seen as an arms race AI can easily game.
• Some think the realistic long‑term answer is more small, tutorial‑style classes where social pressure and direct questioning make cheating harder—but these are very expensive.

Purpose of Education vs. Grades and Credentials

• Several comments stress that students cheat because grades and credentials are rewarded more than knowledge; AI just makes this misalignment obvious.
• Higher education is criticized as a “qualification factory” and an industry optimized for ROI and debt extraction, especially in large, impersonal 101 courses.
• There’s debate over humanities requirements: some see cheating there as predictable given cost and irrelevance; others insist humanities and critical thinking remain essential.
• Goodhart’s law is invoked: once grades are the measure of knowledge, systems optimize for grades, not learning.

AI’s Impact on Learning and Critical Thinking

• One camp is deeply worried that pervasive LLM use will erode critical thinking, especially for students who lean on AI as a crutch.
• Others argue every technological shift (printing press, calculators) sparked similar fears; the real task is to integrate AI intelligently.
• Suggested “hard solutions”:
  • Assume AI use and ask harder, more open‑ended questions.
  • Shift toward teaching students to teach others, conduct research, design questions, and collaborate with AI at its failure frontier.
  • Use AI as personalized tutors, potentially solving parts of Bloom’s “two sigma problem.”
• Skeptics counter that “just make it harder” ignores scaffolding and developmental limits; early foundational skills still need human, low‑tech assessment.

Hiring, Imposters, and Professional Signaling

• Interviewers report spotting candidates reading off LLM‑generated answers in real time, raising concerns about multi‑job “imposters.”
• Others note this just extends longstanding issues of résumé inflation; finding genuine competence has always been hard.
• Suggestions like checking LinkedIn or extensive background validation raise equity and privacy concerns for candidates who avoid social media.

Essays, Writing Craft, and Prestige

• Some celebrate the “death” of the essay as an arbitrary grading tool; others defend essays as unmatched for teaching argument structure and evaluation of sources.
• Anecdotes about pre‑written, memorized exam essays highlight that “gaming” essay systems predates AI.
• There’s a prediction that AI will widen the gap: elite institutions with intensive tutorials will preserve real writing and thinking, while mid‑tier programs struggle to ensure learning “despite AI.”