How Much AI in a Paper? The Core Ideas Should Be 0% (A Practical Boundary)

If you’re searching “how much AI in paper”, you’re probably not looking for a nice number—you’re asking: at what point am I crossing the line?

Here’s the most important sentence upfront: for the “core ideas,” AI usage should be 0%. It can help you write faster and smoother, but it should not do the thinking for you.

1. Ask the “Percentage” Question the Right Way

“how much AI in paper” sounds like a percentage problem. But what actually traps most people is usually two things:

You don’t know what your school/journal truly allows.
You’re not sure whether you still have a real “contribution.”

So people try to comfort themselves with a number: 30%? 50%? “As long as it’s not too much, it’s fine?”

The most dangerous part of academic writing is exactly this:

You think you’re controlling the ratio.
But you’re actually outsourcing your thinking.

So I prefer rewriting the question into something actionable:

Which parts must come from my own thinking—and which parts can AI help me save time on?

Once you can answer that, you don’t need a universal percentage. The real boundary is not word count; it’s responsibility: can you take full responsibility for the most critical parts of the paper?

2. A Baseline I Recommend: 0% for Core Ideas; Use AI Only as Assistance

Let’s be direct:

Research questions, hypotheses, core arguments, method choices, and key derivations—AI’s share should be 0% in these parts.

Not because AI is always wrong, but because if you didn’t lead these decisions, you’ll struggle to stand your ground in a defense, peer review, reproduction, or any serious questioning.

So what can AI do? A lot—if it stays a time-saving assistant rather than a replacement author. Here’s a rough spectrum (from safer to riskier): the further you go, the more careful you must be, the more disclosure you may need, and the easier it is to cross the line.

2.1 Time-saving “tool work” (usually low controversy)

Use AI confidently for form—not contribution. For example:

Smoothing sentences
Fixing grammar
Adjusting citation formats

The value is simple: it reduces time spent on details, so you can keep your attention on the research.

“Retrieval” can also belong here, with one key constraint:

AI can help shorten your search path.
AI should not decide what you cite.

It can suggest leads and candidate lists, but the final judgment—what to cite and why—must be yours.

2.2 Writing collaboration (usable, but you must keep authorship)

This is the most common layer—and the easiest to overstep. For example:

Generating an outline
Expanding your notes into “paper-like” paragraphs
Drafting an abstract from your existing content

These aren’t automatically forbidden. But there is a hard standard:

You must be able to point to where the ideas come from.

If the ideas come from your experiments, reading, and reasoning, AI is helping you express them more clearly.

If the ideas come from the model’s “spark,” you’ve given away the most valuable part.

Many journals/schools now ask for AI-use disclosure (especially if AI participated in content organization or paragraph drafting). Whether you disclose in Methods or Acknowledgements depends on local rules—but at minimum: you should be able to trace your process.

2.3 Replacement writing (highest risk, least worth it)

The key sign isn’t “does it sound like AI?”

The key sign is: you can’t produce a clear knowledge chain. For example:

Asking AI to generate your hypothesis
Letting AI invent a plausible interpretation for your data
Having AI write a full section and only lightly polishing it

The risk isn’t just detection. The real risk is that when someone asks:

Why this design?
Why this baseline?
What are the boundaries of this conclusion?

…you don’t have real answers.

3. Three Self-Checks That Beat Any “Percentage”

If you want an executable boundary, do a quick self-check after every critical paragraph.

3.1 Traceability: where is the “root” of this paragraph?

You don’t need to turn writing into an audit. But you should be able to answer:

Which paper?
Which dataset?
Which note?
Which experiment?
Which derivation step?

If your answer is “my understanding after reading X” or “my result from my data,” you’re fine.

If your answer is “AI wrote it and it feels right,” stop—not because it must be wrong, but because it lacks a source you can stand behind.

3.2 Replaceability: can you rewrite it in your own words?

This test is brutal but honest.

If you delete a paragraph and you cannot restate its meaning, you didn’t truly understand it. Even if it appears under your name, it’s hard to claim as your contribution.

3.3 Contribution: did you lead the most valuable parts?

AI can help you write more academic, more concise, and even help you anticipate counterarguments.

But what you are proving, why you’re proving it this way, and what you add beyond prior work must be led by you.

4. How to Calibrate in Different Scenarios

4.1 Thesis / dissertation: safest is the most conservative

A thesis is evaluating your research ability, so boundaries are naturally stricter.

Use AI mainly for “time-saving without changing contribution,” such as language polishing, formatting, or restructuring your existing notes into clearer writing.

If you need help with structure suggestions or an abstract draft, align with your advisor first—and make sure you can explain every key claim in the defense: where it came from and why it’s written that way.

4.2 Journal papers: policy first, not “can I pass detection?”

Journal policies vary a lot. The correct move is to read the target journal’s AI policy carefully—especially around authorship, data/figure generation, and disclosure requirements for text generation.

Don’t obsess over “hiding from detectors.” Reviewers care more about whether your argument is sound, your citations are reliable, and your method is reproducible.

AI can reduce retrieval time, but the citation chain, method details, and result interpretation must remain under your control.

4.3 Conference papers: deadlines don’t justify delegation

Time pressure is real, but standards don’t drop because you’re rushing.

The most common failure mode is: AI writes too fast, and you don’t have time to verify the source of every sentence.

If you use AI, use it to reduce boilerplate: let it turn your already-clear points into conference-style paragraphs, then verify sentence by sentence.

4.4 Course assignments: read the rules first

Coursework is usually about training ability, not product delivery. AI policies vary widely.

If allowed, treat AI as a learning partner: ask it to explain concepts, point out gaps in your reasoning, and suggest search keywords—don’t let it do the key derivations for you.

5. A Healthier Collaboration Principle: Use AI to Reduce Non-Core Work

The most stable, least mentally taxing approach I’ve seen is using AI as a “pipeline accelerator”:

Reduce retrieval time
Reduce boilerplate writing
Reduce formatting time

…but keep it away from your core ideas.

5.1 Make the process traceable, not the output “look human”

You don’t need to label every sentence. But you should be transparent to yourself:

Which paragraphs were AI-reorganized?
Which sentences were AI-polished?
What did you change afterwards?

A simple practice is keeping key versions:

your draft → AI-assisted version → your final, verified version

This is especially useful if you’re questioned later.

5.2 Prefer AI to help you “find,” not “invent”

A common low-quality use is asking AI to produce a polished exposition about topic X. It may read well, but it’s hard to verify every claim.

A better use is having AI help you pull together your existing materials—retrieve, connect, deduplicate, and re-express—so the writing is materials-driven, not “model inspiration.”

This also aligns with Notez’s direction: let your notes, papers, and knowledge base participate in writing, so every paragraph can be traced back to your materials rather than relying on the model to make up ideas.

5.3 Give every key conclusion a verifiable citation chain

In the end, writing a strong paper is less about who writes nicer sentences and more about whose chain is clearer:

What does this claim rely on?
Where does this figure come from?
What are the boundaries of this conclusion?

AI can help you express the chain clearly—but the chain must exist.

6. Common Questions (How I’d Answer Them)

Q1: Is using AI for polishing considered cheating?

Usually, no. Language polishing is closer to proofreading and is often accepted.

But if “polishing” quietly changes your logic or adds new ideas, it’s no longer just a language service. Go back to the traceability test: whose idea is it—and can you stand behind it?

Q2: How do journals detect AI-written text?

Detection tools are not consistently reliable, and practices differ across institutions.

More importantly, don’t let “undetectable” become your standard. The only long-term standard is: can you take responsibility for the key content?

Q3: What if my field has no clear rules?

Then choose the most conservative approach—the one you won’t regret:

Write the core contribution yourself
Keep AI involvement traceable
Communicate or disclose when in doubt

Q4: If AI wrote a draft and I heavily edited it, is it “mine”?

It depends on what you changed.

If you mostly rewrote sentences and connectors, you likely still borrowed the content.

If you rebuilt the argument structure, added your own derivations and evidence chain, and treated it as a rough sketch you fully reconstructed, it’s closer to your contribution.

The most stable workflow is simple:

You write the core frame first (problem, method, conclusion, evidence).
Then AI helps reduce boilerplate writing time.

7. Where Notez Fits (and What It Doesn’t Promise)

If you’ve read this far, you may notice the real dilemma:

You want AI to save time, but you worry the output is unreliable, untraceable, and hard to justify.
You don’t want writing to become a game of “beating detectors,” because that doesn’t solve the root problem.

Notez’s idea is simple: let your materials participate in writing, and keep output verifiable. Based on these two concerns, it mainly helps in two ways.

7.1 Reduce “made-up” writing by staying materials-driven

Let’s be clear first: no product can guarantee your writing will never be considered AI-generated. Detection tools, journal policies, and institutional standards can change.

What Notez can do is pull writing back from “model free-play” to a materials-driven process: generated drafts should, as much as possible, be grounded in the documents you provide (papers, notes, experiment records, meeting notes), rather than the model filling gaps by imagination.

This brings two practical benefits:

It fits academic accountability better: your “basis” comes from materials you actually have and can verify.
It’s easier to justify: when asked “where did this sentence come from?”, you can point back to your notes/papers instead of “the model said so.”

7.2 Even if the draft isn’t perfect, you can return to the source

Traceability matters most when the draft isn’t rigorous enough.

A useful system should let you:

locate the original text a summary or claim is based on
rewrite on top of the evidence chain, not on top of “plausible-sounding text”

In writing, that experience often matters more than “a pretty first draft,” because what you ultimately need is a defensible chain:

original text → your understanding → your expression

Minimum use

If you want to use Notez without crossing boundaries, the smallest viable workflow is:

Upload a small batch of materials you’re confident you’ll cite (e.g., 5–20 core papers + your notes).
Ask it to organize, not invent: outlines, key points per paper tied to sources, and terminology consistency.
Verify and rewrite on the source—keeping the “0% core ideas” baseline.

8. Closing

Back to “how much AI in paper”: yes, you can use AI—but don’t treat it as a replacement writer.

A principle that sounds rigid, but is safer in practice:

AI’s share should be 0% in your core ideas.

Use AI to save time on retrieval, formatting, and unavoidable boilerplate. But don’t hand over “what I’m arguing, why I’m arguing it this way” to the model.

If you can explain the source and logic of your key claims, trace your evidence chain, and defend your argument without AI—then AI is assisting you, not replacing you.

If you’re looking for a more controllable workflow—where your notes and papers truly participate in writing rather than relying on the model to generate ideas—Notez’s design philosophy may be worth a look. We don’t aim for “one-click drafts”; we aim to make every paragraph checkable.