Critical Thinking with AI • Hist 300 • Last Day
A semester of readings, dialogues, prompts, broken builds, and one stubborn question: how do we know what we know — and what changes when a machine starts “knowing” too?
Slide 01 · The Course in One Sentence
Whose word counts — and why? Plato asked it about writing. The Royal Society asked it about strangers. Johns asked it about printers. Trouillot asked it about archives. We asked it about chatbots.
The trick is to recognize the question when it shows up in a new costume. If you can do that — you can read AI like a historian, not like a tourist.
Slide 02 · Old Questions, New Costume
Plato: writing will erode memory.
Bacon: don’t trust books, trust your eyes.
Shapin: science runs on the gentleman’s word.
Johns: fixity is a social achievement.
Postman / Nichols: the kids can’t tell experts from posters.
Every new information machine triggers the same panic — and the same scramble to figure out who gets to be a source. AI is the latest installment. Not the first. Not the last.
Slide 03 · What the History Actually Taught
Two copies of a book aren’t reliably identical. Fixity is social labor, not a feature of the press.
Most of what scientists “saw” they got by mail. Credibility was anchored in the gentleman, not in the data.
Whenever “objectivity” looks effortless, somebody is doing a lot of invisible work upstream. That is also true of your favorite model.
Slide 04 · New Questions Only AI Raises
A sentence with no author. A “source” that is the average of a scraped continent. RLHF as an invisible authority. Bias not at the level of a single bigot but baked into a corpus. Fluency that increases as grounding decreases.
You can’t critique what you can’t name. Half the work this semester was vocabulary acquisition: stochastic parrot, jagged frontier, hallucination, hybrid fact, archival silence. Use the words. They earn their keep.
Slide 05 · The Expertise Question
Postman and Nichols were already worried about beer-mat expertise — knowing just enough trivia to sound smart at the bar. AI didn’t cause that. It just industrialized it.
The expertise that survives looks more, not less, like the kind we read about all semester: slow, social, situated, accountable. The kind a model can imitate at the surface and not at all underneath.
Slide 06 · Why We Made You Build a Website
Reading and writing essays uses one circuit. Editing YAML, fixing a broken link, pushing to GitHub uses a totally different one — closer to fixing a bike than writing a paragraph.
AI is happiest in the easy mode — where everything is text and everything is plausible. The build either works or it doesn’t. There is no “sounds about right” in a missing semicolon. The friction was the point.
Slide 07 · The Discomfort Was the Point
The first time the site failed to deploy was probably the first time you really understood what files do. The first time AI got it wrong was probably the first time you noticed your own reasoning. Both of these are gifts disguised as bugs.
Comfort is the enemy of skill. Don’t bring a forklift to the weight room. You came here to lift.
Slide 08 · The Driving Question, Revisited
“How can AI help us think more critically, develop skills, and produce higher-quality work than we could without it?”
Both. AI is most useful when it gives you better questions, not better answers — and you only know it’s a better question if you brought your own. The course is the place to practice bringing your own.
Slide 09 · Habits Worth Keeping
Don’t take any single source’s word for it — not the model’s, not the textbook’s, not mine.
The smoother the answer, the more it has hidden. Smoothness is a clue, not a virtue.
Every claim has an address — a printer, an archive, a server farm, a labeler in Nairobi.
The artifact isn’t the value. The process is. AI raises the bar for proving the process is yours.
Slide 10 · Questions Worth Keeping
The Hope
AI in two years will be unrecognizable. The questions won’t. If you walk out asking better ones — out loud, in writing, of yourself, of the model — the course did its job.
Thanks for the semester. Now go break something on purpose.