Trying to teach in the age of the AI homework machine

AI, Homework, and Assessment

• Many see graded take‑home work as untenable: LLMs can do most essays and coding assignments, making homework a poor proxy for understanding.
• Common proposed fix: shift weight to in‑person, proctored assessment—handwritten exams, lab tests on air‑gapped machines, oral exams, in‑class essays, code walk‑throughs, and project defenses.
• Objections: this is more expensive, harder to scale, and clashes with administrative pushes for “remote‑friendly” uniform courses and online enrollment revenue.
• Some instructors respond by massively scaling assignment scope assuming AI use; critics say this effectively punishes honest students and those unable or unwilling to use AI.

Is AI Use Cheating or a Job Skill?

• One camp treats AI as a natural tool like a calculator or IDE: let it handle boilerplate, glue code, proofreading, and use freed time for higher‑level skills.
• Others argue that if AI is required to keep up, non‑users are disadvantaged, and students can pass without building foundational competence.
• Suggested middle ground: allow AI for practice and exploration but verify mastery in AI‑free settings; or use AI as a “coach” (e.g., critique a student’s handwritten draft) rather than a ghostwriter.

Re‑thinking Homework and Grading

• Many commenters say homework should be mostly ungraded or low‑weight, serving as practice plus feedback rather than evaluation.
• Others note that graded homework exists largely to coerce practice; when AI completes it, students still fail exams but expect make‑ups.
• Variants proposed: nonlinear grading (final = max(exam, blended exam+HW)), frequent low‑stakes quizzes, large non‑AI‑solvable projects, or flipped classrooms where practice happens in class and “lectures” happen at home.

Value of Degrees and Institutional Incentives

• Some predict widespread AI cheating will make many degrees indistinguishable from degree‑mill credentials; others think “known‑rigorous” institutions that lean on in‑person testing will become more valuable.
• Multiple threads blame the consumer/for‑profit model: funding tied to graduation counts, online enrollment as a “cash cow,” grade inflation, and admin‑driven constraints (e.g., banning in‑person exams for “fairness” to online sections).
• Several teachers report AI has exposed pre‑existing problems: weak motivation, cheating cultures, and an overemphasis on grades and credentials over actual learning.

AI as a Tutor vs. AI as a Crutch

• Individually, many describe LLMs as transformative for self‑study (math, CS, Rust, etc.), especially for motivated learners and adults without access to good teaching.
• The tension: AI can be an extraordinary personal tutor, but in credential‑driven systems students are heavily incentivized to use it as a shortcut, hollowing out the meaning of coursework unless assessment is redesigned.