Implications of AI to schools

Shift to In‑Class, Device‑Free Assessment

• Many argue schools must assume all take‑home work is AI‑assisted, so real evaluation moves to supervised, in‑person settings.
• Proposed implementations: blue‑book style handwritten exams, phone bans, air‑gapped school computers, or tightly locked‑down Chromebooks.
• Critics note handwriting speed and fine‑motor issues make this unfair for some students, and oral exams cause anxiety and don’t scale to large classes.

Homework, Flipped Classrooms, and What Learning Is For

• One camp sees AI as the death of traditional homework and essays; others see an opportunity for “flipped classrooms” where home is for reading/AI‑aided exploration and class is for assessed work.
• Some suggest not grading homework at all: it’s for practice; only in‑class tests count. Cheating then mainly harms the student, not the grade.
• Underneath is a deeper argument: is schooling about genuine learning, or about credentials that gate jobs and university access?

AI Detection Tools and Due Process

• Strong pushback against AI‑detection products (e.g. Turnitin’s AI flags): 80% “accuracy” is seen as meaningless without false‑positive rates.
• Numerous anecdotes of honest work being flagged (including quoted text, repeated prompts, or a student’s own earlier papers).
• Commenters stress that these tools are often treated as oracles, inverting “innocent until proven guilty” and forcing students to prove authorship via oral defenses, edit history, or even video evidence.
• Several call for banning such tools or suing vendors; others propose using them only as weak signals, never sole proof.

Redesigning Assignments and Exams

• Suggested adaptations:
  • More in‑class essays, code demos, project defenses, and random orals.
  • Grading the process (Google Docs history, keystrokes, student–LLM dialogue) rather than just final output.
  • AI‑administered oral quizzes as a scalable “daily viva” with spot checks by humans.
• Skeptics worry about time, cost, subjectivity, and an arms race (AI tools that fake human revision patterns or keystrokes).

Teachers, Workload, and Systemic Constraints

• Many note that better assessment (orals, project reviews) is possible but extremely labor‑intensive; mass education has favored cheap methods (scantrons, standardized tests, big lectures).
• Some report teachers already using LLMs to grade, creating “LLM writes, LLM grades” loops. Others see LLM‑assisted grading as acceptable if teachers review quickly, improving feedback speed.

AI as Tutor vs. AI as Shortcut

• Optimists see AI as a “digital tutor” that can personalize explanations, smooth learning curves, and support project‑based education.
• Pessimists emphasize that most students are not intrinsically motivated and will primarily use AI to avoid effort, hollowing out real skill acquisition.
• Several conclude the real winners will be students who use AI to deepen understanding, then demonstrate competence in AI‑free settings.

Equity, Credentials, and the Future of Schooling

• There’s concern that if authentic evaluation requires small seminars and orals, education becomes more like Oxbridge: higher quality but much more expensive and elitist.
• Others predict more reliance on high‑stakes, in‑person standardized exams (for university admission and hiring) as grades and coursework become less trustworthy.
• Overall, commenters expect AI to break many existing practices (take‑home essays, cheap mass grading) before better, scalable models of teaching and assessment are widely built.