Students fight back over course taught by AI

AI as Teacher vs Tool

• Many see AI-generated slides and voiceovers as a UX and pedagogical failure, not just an ethical one: teaching is described as inherently bidirectional, relying on feedback, diagnosis of misunderstanding, and context.
• A minority argues teaching need not be bidirectional if technology is properly structured, but this view is strongly challenged as ignoring what good teaching actually is.
• Several note that AI is appropriate for scaffolding, content generation, or private tutoring, but not as a primary paid instructor.

Quality and Purpose of University Education

• Widespread criticism of existing university teaching: rushed, untrained, research-focused staff yielding lectures worse than high‑quality YouTube content; “learning theatre.”
• Others counter that this is an overgeneralization: at decent institutions many professors and especially lecturers put serious effort into teaching, and YouTube is often shallow or infotainment-focused.
• Students value universities for accountability (grades, deadlines), community, mentorship, networking, and “growing up,” not just content.

Economics, Austerity, and “Skimpflation”

• AI use is framed as another form of austerity/shrinkflation: replacing the hard, expensive parts of education (feedback, grading, live teaching) first, while marketing the same product.
• Commenters link rising costs to structural forces (e.g., Baumol effect) and to the commercialization of universities, heavy reliance on fee‑paying international students, and growth‑obsessed administrations.
• Adjunctification and multi-campus gig teaching are highlighted as symptoms of cost-cutting and degraded working conditions.

Equity and Stratification

• Many predict AI will deepen stratification: typical students get cheap AI content; wealthy students get human tutors and small-group teaching.
• Others argue that cheap AI tutors might help under-resourced students more than rich students already near their learning ceiling.

Trust, Quality, and Ethics of AI Content

• Personal anecdotes describe powerful, tailored learning experiences with LLMs (e.g., understanding a rare disease), but pushback emphasizes hallucinations and unverifiable “insights.”
• Strong criticism of educators passing off unreviewed AI output as their own: if a teacher can’t outperform an LLM, their value is questioned.
• Ethical concerns include potential fraud if AI-delivered teaching wasn’t disclosed, IP ownership of student work sent to third-party models, and the prospect of “AI students submitting AI papers to AI teachers.”