AI 101: What Counts as AI?
Module 3 · Spotting Imposters

3.2 Reflect: Watch for Red Flags

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IN THIS LESSON

You just tagged the hype. Now capture the one red-flag phrase that fooled (or alerted) you—so you never overlook it again.

Copy and complete this template:

Red-flag phrase:

“I spotted _______________________________.”

Why it worries me:

“If I ignore that phrase, we might _______________________________.”

My future filter (≤ 25 words):

“Next time I see it, I will _______________________________.”

Strong Reflection Examples

Technical Red Flag:

  • 🔴 "I spotted 'proprietary AI logic' describing basic scheduling rules."

  • 💡 "If I ignore that phrase, we might think automated schedules are actually learning from student success patterns."

  • ➡️ "Next time I'll ask: 'How does your AI improve its predictions over time?'"

Marketing Red Flag:

  • 🔴 "I spotted 'next-generation AI solution' with no mention of data or training."

  • 💡 "If I ignore that phrase, we might buy expensive software that can't actually adapt to our students."

  • ➡️ "Next time I'll demand: 'Show me your model's training process and data sources.'"

Contradiction Red Flag:

  • 🔴 "I spotted 'AI-powered automation'—which is an oxymoron."

  • 💡 "If I ignore that phrase, we might confuse rigid rules with adaptive learning."

  • ➡️ "Next time I'll clarify: 'Is this learning from data or following programmed rules?'"

Example Completed Reflection:

🔴 Red-flag phrase: "I spotted 'revolutionary AI engine' with zero technical details."

💡 Why it worries me: "If I ignore that phrase, we might waste budget on glorified spell-check thinking it's adaptive learning."

➡️ My future filter (≤ 25 words): "Next time I see it, I will ask 'What specific type of AI model and training data do you use?'"

Common Red Flags to Watch For

Empty Buzzwords:

  • Revolutionary/Cutting-edge/Next-generation AI

  • Smart/Intelligent/Advanced (without specifics)

  • Proprietary algorithm (often means basic code)

Impossible Claims:

  • Understands every student (how?)

  • Perfect accuracy (red flag!)

  • No training needed (all AI needs training)

Contradictions:

  • AI-powered automation (pick one)

  • Instant AI analysis (inference isn't training)

  • Rule-based machine learning (nope)