critical-thinking
5 free lessons tagged critical-thinking across AI, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
What This Teaches About Measuring Anything
The exchange is a case study with transferable rules. A conclusion resting on failures needs a failure taxonomy. Every instance must be verified solvable before anyone is scored against it. Output format is a confound whenever answers get long. And when two explanations fit the same data, the productive move is to find the prediction on which they differ, then test it.
What a Percentage Does and Does Not License
A model went from 27 percent to around 57 percent, so it is more than halfway to AGI and the rest arrives shortly. That inference is wrong in at least four ways, and working through why is more useful than the score itself. This lesson covers the linearity assumption, construct validity, contamination, and what the framework is good for once you stop reading it as a progress bar.
What Happens to Your Own Thinking
Delegating cognitive work has effects on the delegator. This lesson covers cognitive offloading and what is known about it, why the tasks that feel wasteful are often where skill is built, the expertise paradox in who benefits, and how to decide what to keep doing yourself.
Checking, When Checking Costs More Than Generating
Verification is now the expensive step, which changes what a sensible checking strategy looks like. This lesson covers the asymmetry between producing and refuting, deciding what to check before you read it, the questions that actually discriminate, and what a citation is worth.
Why Fluent Text Defeats Your Judgement
Generated output is persuasive through properties unrelated to whether it is true. This lesson covers processing fluency, automation bias, the illusion of explanatory depth, and sycophancy: four mechanisms that make a confident draft harder to evaluate than a hesitant colleague.

