Turn Any Article into Cornell Notes with AI

The Cornell Format Explained

Walter Pauk developed the Cornell notes method at Cornell University in the 1950s. It’s survived six decades because it works — the format builds review and self-testing directly into the note-taking process.

A Cornell note page has three sections. The main notes column (right side, about 2/3 of the page) captures key points during reading. The cue column (left side, about 1/3) holds questions and keywords added after reading. The summary section (bottom) distills everything into 2-3 sentences.

The magic is in the cue column. Those questions transform your notes from a passive record into an active recall tool. Cover the main notes, read a cue question, try to answer from memory. This simple practice — called retrieval practice — dramatically improves retention compared to re-reading.

AI accelerates the process. Instead of manually creating cue questions after reading, you get all three sections instantly. Your job shifts from creation to curation: refine the questions, add your own connections, rewrite the summary in your voice.

The Cornell Notes Prompt

The Cornell notes prompt (PR030) asks AI to structure any article into the three-section format. It produces main notes as concise bullet points, cue questions that match each point, and a summary written for someone who hasn’t read the original.

The output gives you a complete first draft. But the learning happens when you edit it. Rewrite cue questions using your own language — questions you write yourself trigger memory better than questions someone else wrote. Add connections to things you already know. Rewrite the summary without looking at the AI’s version.

This is the key principle: AI handles extraction, you handle encoding. The AI can identify what’s important faster than you can. But only you can connect it to your existing knowledge, phrase it in words that stick for you, and actively retrieve it during review.

Example: Cornell Notes in Action

Say you read an article about cognitive load theory. Here’s what the output might look like:

MAIN NOTES (Right Column):

• Working memory holds 4±1 chunks at once
• Three types: intrinsic (material complexity), extraneous (poor design), germane (learning effort)
• Reduce extraneous load first — it’s “waste”
• Intrinsic load can’t be reduced, only managed through sequencing
• Germane load is good — it’s actual learning happening

CUE COLUMN (Left Column):

• How much can working memory hold?
• What are the three types of cognitive load?
• Which type should be reduced first?
• Can intrinsic load be eliminated?
• Why is germane load “good”?

SUMMARY (Bottom):

Cognitive load theory explains why learning fails when working memory is overwhelmed. Designers should minimize extraneous load (distractions, poor formatting) while accepting intrinsic load (material difficulty) and maximizing germane load (actual thinking about content).

Notice how each cue question maps to specific notes. During review, you’d cover the right column, read “What are the three types of cognitive load?”, try to recall all three, then check your answer.

When to Use Cornell vs. Other Note Systems

Use Cornell notes when: You need to study and review material. The cue column makes review active, not passive. Ideal for academic articles, technical content, or anything you need to remember.

Use flashcards when: You need to memorize discrete facts or want to use a spaced repetition app. See Flashcards from Reading (C022).

Use Zettelkasten when: You’re building a permanent knowledge base organized by concept rather than source. See Zettelkasten from Highlights (C023).

Explore more note-taking systems in the Notes & Memory pillar or return to the AI for Reading hub.