This is the home page of the course syllabus, which outlines all the instructions, logistics, and expectations for the course. The syllabus also has a schedule page, which details the reading and activities for each session. Course slides are available as a click-through Reveal.js deck.
How do we know what we know—and what happens when machines start “knowing” things too? (and: do they?)
This course uses historical practices and narratives as a laboratory for exploring how AI can sharpen critical thinking and produce higher-quality intellectual work. Rather than treating AI as a shortcut or a threat, we treat it as a thinking partner: a tool that, when used deliberately, makes visible our own reasoning processes and exposes the assumptions we bring to interpretation, evidence, and argument.
Students will practice making sense of the past with the help of AI. The question we explore: how can it actually be helpful, as opposed to just stringing together words that sound authoritative? Some activities: finding sources, evaluating trustworthiness, synthesizing evidence, constructing arguments, identifying omissions. We constantly work alongside AI, learning when it helps, when it misleads, and what the difference reveals about how knowledge and expertise actually work.
How can AI help us think more critically, develop skills, and produce higher-quality work than we could without it?
These indicate something you have to DO or TURN IN.
These indicate something you should be aware of—usually an upcoming assignment or a longer reading—but isn’t anything you need to immediately do.
These indicate something that is important to know, but isn’t time sensitive.
All readings are available online or through Zotero. You never need to find anything!
We use a tool called Zotero to organize and provide access to all readings for the course. To get connected, carefully follow the getting started guide. If it doesn’t work for you, please follow the directions more carefully. They’ve worked for hundreds of students!
ai-critical-thinking-unm (in case you try to look it up on zotero.org, but you shouldn’t need to.)You get graded on effort on this class, and the way you show effort is to show your work. It’s like math class, but without the math. I only care about the energy you put into an assignment and learning to use AI effectively, not what the final product is. It’s the process that’s important. You need to be sure I can see your process.
All work is graded on the following scale:
| Perceived Effort | Grade | Grade points |
|---|---|---|
| Very fine | A | 4 |
| Fine | B | 3 |
| Marginal | C | 2 |
| Redo | F | 0 |
Your final grade is simply the average of all your work (with assignment weights factored in), plus any extra credit from reading assignments and whether you’ve been a regular contributor to class discussions.
| Grade points | Grade |
|---|---|
| 4.0 | A |
| 3.7 | A- |
| 3.3 | B+ |
| 3.0 | B |
| 2.7 | B- |
| 2.3 | C+ |
| 2.0 | C |
| 1.7 | C- |
| 1.3 | D+ |
| 1.0 | D |
| 0.0 | F |
For almost everything we read, we’re reading to ENGAGE with it, not because it’s right. There is a LOT to disagree with across the readings, and we don’t all have to agree on everything. The goal is to develop frameworks for thinking critically about AI, knowledge, and expertise.
If life gets overwhelming during the course, please reach out. We can discuss formal or informal accommodations, deadline adjustments, or other support. The goal is maximizing learning under real life circumstances.