🔍 Critical Thinking: Ask Better Questions and Verify Information in the AI Era

By Better Wise · Generated by Everwise

Learn to ask sharper questions, recognize logical fallacies, and verify information before believing or sharing it. Build a practical process for evaluating AI-generated answers using credible sources, reliable evidence, and realistic confidence levels.

Level
Beginner
Goal
Fundamentals
Language
English
Focus
Asking precise and productive questions Verifying AI-generated claims with primary sources Recognizing logical fallacies and cognitive biases Evaluating source credibility and evidence quality Calibrating confidence and acknowledging uncertainty
Practice
Guided exercises
Time
~5h
Voice
A skeptical investigative journalist who turns everyday claims and convincing AI answers into evidence hunts, teaching learners to challenge ideas without becoming cynical.
Not covered
Formal symbolic logic, Academic research methodology, Advanced statistical inference, Political persuasion and ideological debate

What you will achieve

By the end of this course, you will be able to do things that most people only talk about doing. You will read a claim and immediately ask the question a working journalist asks: where does this come from, and who says so?

Specifically, you will be able to:

  • Separate facts, opinions, and testable claims, and tell which kind of statement you are looking at.
  • Name the logical fallacies that show up in everyday arguments and spot them in the wild, not just in a textbook.
  • Recognize your own cognitive biases and motivated reasoning, including the moment you start looking for evidence that confirms what you already believe.
  • Apply the SIFT method (Stop, Investigate the source, Find better coverage, Trace claims) and lateral reading to check a claim before you share it.
  • Judge source credibility by looking at who is behind a source rather than how polished the page looks.
  • Trace a claim back to its primary source, and notice when the trail goes cold or the original says something different.
  • Use claim, evidence, and reasoning to cross-check evidence against a conclusion.
  • Verify AI-generated answers instead of trusting a confident-sounding chatbot.
  • Express calibrated confidence, so you can say "I am fairly sure, and here is why" rather than guessing at certainty.

The goal is not to make you a cynic who trusts nothing. It is to make you a skeptic who trusts things for good reasons.

Who this course is for

This is a beginner course. No prior knowledge of logic, statistics, media studies, or research methods is assumed. If you can read a news article and use a search engine, you have everything you need to start.

It is for you if:

  • You have shared something online and later found out it was wrong.
  • You have gotten a confident answer from an AI tool and wondered whether to believe it.
  • You want a repeatable method for checking claims instead of relying on gut feeling.
  • You are tired of arguments where nobody can agree on what counts as evidence.

It is not a course about winning debates or about memorizing a list of fallacies for a test. The skills are practical and you will use them on real claims from the first lesson.

Course roadmap

Module 1: Claims, Evidence, and Fallacies. You start by sorting statements into facts, opinions, and testable claims, then learn the common logical fallacies that make weak arguments sound strong. The module closes with cognitive biases and motivated reasoning, because the hardest person to fact-check is often yourself.

Module 2: The Verification Toolkit. This is where you get hands-on. You practice SIFT and lateral reading on real claims, learn what actually makes a source credible, and follow claims back to their primary sources. You will see how a claim can drift as it gets repeated.

Module 3: Cross-Checking, AI Answers, and Calibrated Confidence. You learn to structure your checking with claim, evidence, and reasoning, then apply it to AI-generated answers that sound authoritative but may be wrong. The final lesson is about calibrated confidence: knowing how sure you should be, and admitting when the evidence does not support a conclusion.

Each module ends with a checkpoint that quizzes what you just learned, so gaps get caught before you build on them.

How the course works

The course runs about 300 minutes across 12 lessons, so roughly five hours of focused work. You can spread that over a week of short sessions or a few longer ones.

Lessons come in a few formats:

  • Short theory lessons that explain one idea at a time, with real examples rather than abstract definitions.
  • Guided practice where you apply the method to actual claims, sources, and AI answers. These are the core of the course, so budget real time for them.
  • Checkpoint quizzes at the end of each module to test whether the skills stuck.

The lessons are designed to be taken in order. Each one builds on the vocabulary and habits from the one before, so skipping ahead means missing the reasoning behind the method. That said, if you already know a topic well, the checkpoint quizzes are a fair way to confirm it and move on.

One suggestion before you begin: keep a browser tab open. You will be checking real claims, and the course works best when you actually look things up instead of taking our word for it.