Critical Evaluation

Use Cases · beginner

AI Literacy Topic 1, Track 2: learn to verify AI output before you trust it — spotting hallucinations, bias, and confident-but-wrong answers. 24 quizzes, 7 questions each — every question includes a short article and a workplace scenario to read first.

Quizzes in this topic

AI Can Be Confidently Wrong: Treat Output as a Draft, Not a Fact
Confident Tone, Wrong Answer: Why AI Certainty Is Never Proof
AI Hallucinations: Spotting Confident, Made-Up Answers
Don't Trust the Numbers: Checking Figures in AI Output
Check the Quote: Verifying Quotes and Attributions Before You Repeat Them
Trust, Then Verify: Cross-Checking AI Claims with a Second Source
Spotting Internal Contradictions
Saying "I Don't Know": How AI Hedging Signals What to Verify
One-Sided Answers: Detecting Bias and Missing Perspectives in AI Output
Does the Answer Actually Address the Question?
Fake Citations and Plausible Links: Checking Whether AI Sources Actually Exist
Show Your Work: Checking the AI's Step-by-Step Reasoning
Spotting the Too-Smooth Answer: Filler vs. Substance in AI Output
Frozen in Time: Spotting Outdated AI Knowledge
Over-Reliance and Automation Bias: Keeping Vigilance When the AI Is Usually Right
Plausible-but-Wrong: Spotting Fake Technical Explanations
Checking AI Summaries Against the Original
Trust It or Verify It? Calibrating Your Verification to the Stakes
Accountability: You Own What the AI Drafts
Hunting Buried Errors in Long AI Outputs
Adversarial Follow-Ups: Stress-Testing AI Claims
Building a Personal Verification Checklist
AI in High-Stakes Domains: Review Gates, Guardrails, and Expert Accountability
Leading a Verification Culture: The Capstone

Every topic has slides to teach you, then quizzes to prove you know it. Retake anytime to raise your score.