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. Early modern Book buyers asked it about printers. Trouillot asked it about archives. We asked it about chatbots.
What is expertise — and why? The point isn't a definition but functionality.
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 simiilar optimism and panic — and the same scramble to figure out who/what is trusted. AI is the latest installment. AI seems like a technical thing---but it’s really a social one, as has always been true.
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, social networks do 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 and filtered by social guardrails. 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. The words help with critical thinking.
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 scales this up and industrialized it.
The expertise that survives looks more, not less, like the kind we read about all semester: earned, social, situated, accountable. The kind a model can imitate only at the surface.
Slide 06 · Why You Build a Website
Reading and writing essays uses circuits that you've already worn in. Editing YAML, fixing a broken link, getting an image to show up, uses a totally different set of circuits.
AI is happiest in the easy mode — where everything is text and everything is plausible. Yet either the build works or it doesn’t. There is no “sounds about right” in a missing quote. The attention to detail was the point.
Slide 07 · Discomfort is necessary
The first time the site failed to deploy was maybe the first time you really understood what files do. The first time AI got it wrong was maybe the first time you noticed your own reasoning. Lessons 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?”
AI is most useful when it gives you better questions, not better answers.
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 hides. Smoothness is a clue, not a virtue.
Every claim has an address — a printer, an archive, a server farm, a labeler in Nairobi.
How can you elevate your own work?
Slide 10 · Questions Worth Keeping
The Hope
Thanks for the semester. Keep learning.