College instructor turns to typewriters to curb AI-written work
Educators are experimenting with low-tech tactics like in‑class handwritten work and even typewriters to counter AI‑generated assignments and restore confidence in assessment. Commenters debate whether high‑stakes, proctored exams, oral defenses, and paper-based workflows are a necessary return to fundamentals or anachronistic in a world where AI will be ubiquitous in work. Underneath is a deeper argument about what universities should signal—genuine mastery versus mere credentialing—and whether curricula should resist, restrict, or deliberately integrate AI tools into how students learn and are evaluated.
Assessment in the age of AI
- Many instructors are shifting back to in‑person, paper-based quizzes and exams to make AI‑assisted cheating harder.
- Some already had “AI‑proof” structures: heavy weight on proctored written exams, projects defended line‑by‑line in person, or handwritten coding exams.
- Others argue too much exam weight is unfair (high‑stakes, time‑pressured, artificial compared to real work where references and tools are allowed).
Exams vs. “real life”
- One side: real work often allows Googling/LLMs; exams and whiteboard interviews are unlike anything in adult life, so designing around them is misguided.
- Other side: many roles require fast recall and reasoning under pressure (incidents, exec meetings, interviews); exams are a proxy for this and for verifying individual competence.
- Several note the real problem is poorly designed exams, not exams per se.
Oral and in‑person evaluation
- Some report systems where oral exams determine most of the grade; cheating is rare but bias risk is high, especially when a single professor controls a mandatory course.
- Defenders say commissions and written records mitigate abuse; critics say power dynamics still make contesting bias risky.
AI: ban, ignore, or integrate?
- “Ban AI” camp: AI lets students skip the learning process, devalues degrees, and harms honest students (especially under curves).
- “Integrate AI” camp: like calculators or compilers, AI should be taught as a core tool; design assignments where using AI still requires understanding, or where AI output is only a starting point.
- Some propose splitting: early years focus on fundamentals without AI; later years focus on doing harder work with AI.
Tool analogies and equity concerns
- Frequent comparisons to calculators, tractors, gyms, and running water; disagreement over whether LLMs are comparable, since they’re non‑deterministic and usually subscription‑based.
- Requiring paid AI tools is seen as widening inequality; others note local/cheaper models exist but may not match top-tier systems.
Cheating, credentials, and labor market
- Widespread AI‑assisted cheating plus weak enforcement may push employers to rely more on their own high‑stakes screening.
- Some argue many white‑collar jobs demand little true competence anyway; others expect AI will expose and eliminate low‑value “text-shuffling” roles.