Last verified 23 September 2026. Springer Nature permits language polishing, wants summaries and literature comparisons disclosed, and forbids AI authors and invented data or citations. The DFG, Germany's main research funder, asks applicants to say which AI they used and why, except for spell checks. Details for experts at the end of this book.

The idea in one page

Research is the one place where how you got a result is part of the result. So the question isn't only "is the output right?" but also "could I describe exactly what the tool did?" Three zones cover most of what you'll meet:

• Usually fine: language, code, triage. Tightening your own sentences, drafting a plotting script (a short program that draws a figure) you then check, sorting a set of abstracts against criteria you wrote. The thinking stays yours, and you can check the output.
• Contested: substantive text. Letting the assistant draft an interpretation, summarise the state of research or compare your results with the literature. Publishers allow it with oversight and disclosure, but it's where reviewers look hardest, and where invented claims creep in.
• Not allowed: authorship and other people's work. AI is never an author, because it can't take responsibility. Fabricated citations are misconduct, whoever typed them. And a manuscript you're reviewing, or a proposal you're assessing, must never go into a public AI tool; some funders forbid AI in reviewing altogether.

The rule, whatever the venue: every AI use that touched the content is described specifically, in the place the venue asks for, and you checked everything it produced. Venues, meaning the journals and funders you submit to, differ in the details. The DFG doesn't want to hear about spell checks. The publisher Springer Nature encourages you to disclose even language polishing. That's why you read the actual policy (the Springer Nature policy (opens in a new tab) is a clear example) rather than guess.

The use log. You can't write a specific statement from memory. Keep one line per use, as you go:

Five fields and a date. It takes thirty seconds per use and saves an evening of guessing at submission.

Worked example: two research situations, four quadrants

The method comes from T01-L01 · From Idea to Working Result: The Four-Quadrant Method. Here it is on a grant proposal, then on a manuscript.

Research situation 1: triaging twenty papers for a grant proposal

Structure it. "For the state-of-research section of my DFG proposal I have twenty candidate papers from a PubMed search. I need to know which ones directly address my question (does protein X regulate autophagy in neurons?) so I can read those in full first. A useful result: a reading order I can defend, with nothing dropped silently."

Understand it. She pastes a few titles into the assistant and asks which are relevant. It confidently adds a twenty-first paper that "also shows this". The paper doesn't exist. Without the paper in front of it, the assistant filled the gap with a plausible guess (T01-L01 · From Idea to Working Result: The Four-Quadrant Method, fact 1). She also realises that "relevant" was her word, not a criterion. Back to Structure it: "Give it the abstracts, not titles. Define 'addresses my question' as three yes/no criteria. It sorts; I decide."

Choose tools. She goes down the ladder from T01-L01 · From Idea to Working Result: The Four-Quadrant Method, starting with the simplest thing that could do the job. PubMed already exports all twenty abstracts in one text file. The institute's approved assistant sorts them, because this is going into an unpublished proposal. Nothing bigger, such as a search tool or an add-on for her reference manager, is needed for twenty papers. The final inclusion decisions and the reading stay hers.

Build it. She exports the abstracts from PubMed: above the search results she clicks Save, picks Abstract (text) as the format and clicks Create file. Each one carries its PMID, PubMed's ID number for the paper. She attaches the file and asks:

The table comes back with all twenty PMIDs. Seven have three yes answers. She checks the quoted sentences for the seven and for every "no": two quotes don't support the answer given, and one paper marked "no" for neurons actually used primary cortical cultures. She corrects those three by hand. One paper moves onto her list, so she reads eight in full. Her log line: "23 Sep · Institute assistant (model not shown) · Triage 20 abstracts against criteria A–C · PubMed export, PMIDs listed · Reading list of 8, my decisions · Checked every quote; corrected 3."

Her disclosure in the proposal: "We used [institute assistant] to pre-sort 20 abstracts against three stated criteria. All classifications were checked against the abstracts by the applicant, and all included papers were read in full." What it taught her: the tool saved an afternoon of scanning, but the checking found a paper she'd have dropped.

Research situation 2: a paragraph and a plotting script for a manuscript

Structure it. "My discussion section is 30 per cent too long, and Figure 3 needs redrawing from the raw data. The target is a Springer Nature journal. A useful result: a shorter paragraph that says exactly what the old one said, a correct figure, and a disclosure I can submit."

Understand it. He asks the approved assistant to shorten the key paragraph. The result is crisp, and it has turned "is associated with" into "drives". That's a new causal claim he never made. The plotting script it drafts runs, but on closer look it averages the three replicates before plotting, so the individual points he wanted are gone. Back to Structure it: "Shorten without changing any claim, and tell me every wording change that affects meaning. Plot every observation; no silent averaging."

Choose tools. Going down the ladder again: the journal's author guidelines give the word limits and figure rules. The approved assistant does the language pass and drafts the script, because both are checkable. He runs the script on his own computer; if you've never run one, T08-L02 · Get a Script Your Colleague Can Rerun, Without Being a Developer shows how. The interpretation stays his.

Build it. He sends the paragraph with: "Cut to 120 words. Keep every claim, number, hedge and citation. Then list every change that could alter the meaning." It lists two. He rejects one ("suggests" → "shows") and keeps the other. For the figure he asks for a script that plots all individual values, labelled by replicate. He tests it on a small table where he knows the answer: four rows in, four points out. His log gets two lines, one per task. His statement, for the journal's AI declaration field: "The authors used [tool, model] to shorten the Discussion and to draft the plotting code for Figure 3. The authors reviewed all edits against the original text and verified the figure against the source data. The authors take full responsibility for the content."

What it taught him: "tighten" is where meaning changes hide. Next time he'll ask for the list of meaning-changing edits from the start.

In both situations the assistant saved real time, and none of the judgements moved to it. The log made the disclosure a five-minute job.

What goes wrong

Listing AI as an author. Symptom: a co-author suggests thanking "ChatGPT" in the author list or the CRediT roles (the standard list of who did what on a paper). Fix: authors must be able to take responsibility, which a tool can't. Describe the use in the disclosure or Methods instead.

Pasting an unpublished manuscript into a personal account. Symptom: the full draft goes into a consumer chat to "polish the English". Fix: use your institution's approved assistant for unpublished work, or only paste material that is already public (T12-L01 · Decide What You May Paste Into AI at Work).

Relying on generated citations. Symptom: a reference that looks perfect and doesn't exist, or exists and says something else. Fix: never ask the assistant to supply references. Give it the papers, and check every citation against the original.

Disclosing vaguely. Symptom: "AI tools were used in the preparation of this manuscript." Fix: name the tool, the task, the part of the work, and how you checked. Your use log gives you all four.

Reconstructing the log at submission. Symptom: you can't remember whether the tool or you dropped a paper. Fix: one log line per use, written the moment you finish.

Assuming your co-authors agree. Symptom: at submission, a co-author objects to AI in the analysis. Fix: agree what's acceptable at the start of a project, and share the log before the draft goes round.

Using AI on work you are reviewing. Symptom: a manuscript or proposal you received as a reviewer goes into an assistant "just for a summary". Fix: don't. The material is confidential: Springer Nature forbids uploading it to public AI tools, and the DFG allows AI in reviews only under the conditions of its reviewer guideline, never by pasting a proposal into a public tool.

Do it yourself (30 minutes)

Pick one real piece of your work where you used, or plan to use, an AI assistant.

1. Structure it (5 min). Write down the target venue and what AI did or will do: which task, which part of the work.
2. Understand it (10 min). Open the venue's AI policy. Find the lines that apply to your use. Is it allowed, allowed with disclosure, or not allowed? Where does the venue want the statement (form field, Methods, acknowledgements)? If the policy doesn't say, write down whom you'll ask.
3. Choose tools (2 min). Decide where your use log lives: a text file next to the manuscript, or a table in your lab notebook.
4. Build it (13 min). Fill in the log for every AI use so far, from your chat history. Then write the disclosure statement: tool, task, part of the work, how you checked, and that you take responsibility.

Exit check

Written finding: one disclosure statement for a real piece of your work, with:

• the use log behind it, one line per use;
• the policy it was checked against: its name, link, the date you read it, and the sentence that applies;
• the location where the venue wants it, or the person you'll ask.

It passes when a co-author can match every sentence of the statement to a log entry and to the policy.

Rule to remember

You are the author. It is a tool you must be able to describe.

Details for experts

Checked on 23 September 2026. You don't need this to follow the book; it's for setting things up or answering a colleague.

Further reading and tools

Next books

• T15-L02 · Record an AI Step So a Co-Author Can Rerun It: recording model, prompt and version so you can rerun an AI-assisted step.
• T15-L03 · Get an AI Step on Participant Data Reviewed, Then Build It Inside the Approval: participant data, ethics approval and peer review with AI.
• T04-L01 · Ask Your Own Documents and Check Every Citation: questions over your own papers, with quotes you can check.
• T10-L01 · Ask a Spreadsheet a Question and Check Which Rows It Used: the same checking habit for tables.
• T12-L01 · Decide What You May Paste Into AI at Work: what may go into which tool.

Tools

• Claude, ChatGPT: Catalogued. Fine for published abstracts on a personal account; use your institution's assistant for unpublished work.
• Perplexity: Catalogued. Search with sources, for finding papers. Check every source it names.