The AI zombification of universities

Authenticity of the Essay & Writing Quality

• Several commenters debate whether the essay itself is AI-generated.
• Some see stylistic tells (em‑dashes, “purple prose”) as possible LLM output; others argue current models still lack its “melodious” style and originality.
• Consensus is inconclusive; style is viewed as a poor diagnostic for AI authorship.

What Universities Will Look Like in 10 Years

• Some expect universities to look mostly the same, with slightly stricter exam rules.
• Others think this would render them increasingly irrelevant to post‑graduation reality.
• There is curiosity about which subjects will still require in‑person teaching and what new course types might emerge.

Assessment, Cheating, and “No‑Tech” Responses

• Many propose in‑person, proctored, pen‑and‑paper exams, oral exams, and in‑class essays as robust against AI cheating.
• Homework is seen as largely compromised; suggestions include making it ungraded practice and tying grades to in‑class tests based on the homework.
• Some describe historical norms where almost all grade weight was on supervised exams, arguing AI changes little there.
• Objections: heavy testing may favor test‑taking over deep learning, and strict proctoring can become invasive or dystopian.

Credentialism, Prestige, and the Purpose of Higher Ed

• Strong view that universities’ main function is signaling/credentialing, especially at elite schools; AI threatens the credibility of that signal if assessment is compromised.
• Others argue universities should primarily teach critical thinking and support personal growth, not just job prep.
• Debate on whether degrees are already “meaningless” or still crucial as hiring filters.
• Some suggest more apprenticeships, vocational paths, and trade schools as better aligned with labor-market needs.

AI as Tool vs “Zombification”

• One camp sees AI primarily as a “slop” generator that enables low‑effort work and erodes attention spans, risking a “zombified” underclass.
• Another camp, including current students, gives concrete examples of using AI as a tutor, code assistant, and note‑cleanup tool that deepens learning rather than replaces it.
• Several argue that how AI is used (assistant vs answer‑machine) is the real fault line.

Broader Systemic Critiques

• Many note that problems (busywork homework, credential focus, shallow learning) predate AI; AI accelerates existing failures rather than creating them.
• There is concern about tying education to centralized, capital‑intensive AI infrastructure, which could further subordinate universities to external technocratic or corporate agendas.