Everyone is cheating their way through college

AI in Medicine and Other Professions

• Several comments generalize from the article to medicine: people joke about “your next doctor cheating with ChatGPT,” and some seriously predict AI triage and minor prescribing, especially in the US where access is gatekept by prescription rules.
• Others push back: off‑the‑shelf tools work for simple cases, but complex cases and physical work (e.g., surgery) still need experienced clinicians; laypeople can’t tell when a case is non‑trivial.
• Parallel anecdotes in tax prep: front‑line humans are seen as undertrained and just “Googling,” while LLMs sometimes give more coherent, searchable guidance.

Cheating, Learning, and Student Mindset

• Many see pervasive AI cheating as depressing rather than morally outrageous: worry centers on loss of deep reading, critical thinking, persistence, and “academic grit.”
• Some argue humans have always cut corners; AI just lowers friction. Others distinguish between using tools to learn and outsourcing all thinking.
• First‑hand reports from faculty and students: a large fraction of classmates use ChatGPT; some copy–paste entire assignments without reading the output.

Structural Problems in Higher Education

• Recurrent theme: college as an expensive gatekeeping machine for jobs, not primarily for learning. The “return” many seek is the diploma, not the knowledge.
• Complaints about tuition, non‑refundable “bad service,” adjunct precarity, and administrators pushing to pass fee‑paying students and soft‑pedal cheating sanctions.
• Several note professors who barely teach but are protected by tenure, versus others who say institutional pressure now heavily constrains professor authority.

LLMs as Tools, Tutors, and Equalizers

• Some defend LLMs as democratized private tutors, replacing expensive human help and filling in where TAs or professors are inaccessible or ineffective.
• Others counter that current LLMs are poor teachers: they hallucinate, encourage shallow understanding, and make it too easy to “look like” you learned.
• There’s debate over whether using English‑language sources, AI, or multiple textbooks is “cheating” or just smart resource use; many frame it as unfair advantage, not dishonesty.

How Universities Might Adapt

• Proposed responses:
  • More oral/individual assessments and code walk‑throughs.
  • Heavier weighting of in‑person, closed‑book exams, especially in math and engineering.
  • Redesigning assignments to assume AI access and test process: debugging logs, experiments, system‑building, and explanation of how tools were used.
  • Group work, spontaneous interaction, and grading for demonstrated internalization rather than polished artifacts.
• Some educators already allow AI explicitly but require documentation of its use; they report blatant misuse is easy to spot but hard to punish formally.

Labor Market, Credentials, and “Operators vs Engineers”

• Several foresee AI hollowing out junior white‑collar roles; others think macroeconomics and offshoring matter more than LLMs right now.
• Strong distinction is drawn between “operators” who orchestrate tools and “engineers” who understand fundamentals; commenters predict operators will be easiest to replace.
• Degrees are viewed as weak but pervasive filters: often unrelated to job content yet decisive for hiring eligibility. Some argue this drives both credential inflation and student willingness to cheat.