Lesson 1503 of 1570
AI and Bias in College Essays: Why ChatGPT Sounds Like a White 40-Year-Old
AI essay help drifts toward one voice — and admissions officers can hear it. Learn to use AI without losing yourself.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The big idea
- 2college essays
- 3voice
- 4bias
Concept cluster
Terms to connect while reading
Section 1
The big idea
Most LLMs default to a polished, midwestern, middle-aged voice because that is most of their training data. Run your essay through one and your Vietnamese-American grandma stops sounding like your grandma. Admissions readers notice.
Some examples
- Ask ChatGPT to rewrite your draft, then ask it to list every word it changed and why — keep the ones it killed.
- Ask Claude to critique your essay without rewriting it. Critique is honest; rewrites flatten you.
- Ask Gemini to read your draft in your grandmother's voice and tell you where it stops sounding like her.
- Ask Perplexity what 2026 admissions deans are publicly saying about AI-edited essays.
Try it!
Paste your most recent essay paragraph into Claude. Ask only 'what is the strongest sentence and why?' Use that as your new opener.
Key terms in this lesson
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “AI and Bias in College Essays: Why ChatGPT Sounds Like a White 40-Year-Old”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Builders · 7 min
Using AI on college apps without crossing the line
AI can help with brainstorming and editing, but the words on your college essay should still be yours.
Builders · 7 min
AI and bias in image generators: why your CEO is always a white guy
Test the bias in image generators yourself and learn the prompt fixes that help.
Adults & Professionals · 11 min
Bias Considerations in AI Vendor Selection
AI vendors vary in bias mitigation. Selection criteria should include bias considerations, not just capability.