AI Tools for Grant Writing.
A practical, honest guide to where AI genuinely helps across the grant lifecycle — and where it quietly makes things worse. Built from the workflows we teach researchers and research managers at leading universities, with a focus on Horizon Europe, ERC, and MSCA.
By Avi Staiman · Academic Language Experts
What AI can and can't do for your proposal
AI will not write a fundable proposal for you. What it will do is collapse the hours you spend on the slow, mechanical parts of grant work — reading long calls, mapping literature, tightening drafts against word limits, and checking coverage against evaluation criteria. Used well, it buys you time for the thinking that actually wins grants: the novelty, the positioning, the evidence.
Used badly, it injects fabricated references, generic prose, and eligibility drift into exactly the document where those things are fatal. The difference is a workflow that keeps a human accountable at every step — which is what we teach.
Where AI helps — stage by stage
Reading & scoping the call
AI is excellent at turning a 60-page Horizon Europe or ERC work programme into a structured brief: eligibility, scope, evaluation criteria, deadlines, and the specific phrases evaluators look for. Paste the call text (or the relevant section) and ask for a one-page summary against the evaluation criteria — never as a substitute for reading it yourself, but as a map of where to look.
Watch out: Calls change between stages. Re-summarise from the official funder page each time, and never let the model paraphrase eligibility rules without you confirming them against the source.
Ideation & positioning
Use AI as a sounding board to pressure-test your novelty and positioning: ask it to play devil's advocate, to name the three strongest objections a reviewer would raise, and to surface adjacent work you may have missed. This is where general-purpose LLMs (ChatGPT, Claude, Gemini) shine — but every claim about prior work must be verified, because LLMs fabricate citations.
Watch out: Treat every reference the model produces as unverified until you confirm it in a real database. This is the single most common way AI poisons a proposal.
Literature & state of the art
Research-RAG tools (Elicit, Consensus, scite, Perplexity, SciSpace) retrieve from actual paper corpora, so their citations are real. They are the right tools for mapping the state of the art, finding consensus and dissent, and building a defensible literature base — far safer than asking a chatbot to summarise the field.
Watch out: RAG tools still miss papers and miscategorise. Use them to accelerate discovery, then confirm coverage with your own database searches.
Drafting & structuring
AI accelerates the hardest parts of drafting: turning bullet points into coherent narrative, tightening a section against a word limit, and aligning language with the evaluation criteria. The productive pattern is to draft from your own ideas and evidence, then use AI to reshape, condense, and stress-test — not to generate substance you can't defend.
Watch out: Reviewers can spot generic, AI-flavoured prose. Keep your voice, your data, and your specific claims; let AI shape, not originate.
Review & compliance
AI is strong at the mechanical review that eats hours: checking the proposal against each evaluation criterion, flagging vague claims, counting characters and pages, and surfacing internal contradictions. A final pass that asks 'which criteria are weakest?' turns a generic draft into a fundable one.
Watch out: Compliance with funder rules (page limits, font, ethics, data management) still requires the official template. AI can draft the Data Management Plan narrative, but the structure must match the funder's.
Budget, partners & project management
For research managers and grant officers, AI helps draft justification narratives for budget lines, build partner descriptions from public materials, and turn work-package tables into readable task descriptions. This is the heart of the research-manager workflow.
Watch out: Never paste a partner's confidential financials or an unpublished proposal into a public chatbot. Use institutional or enterprise accounts where data isn't used for training, or redact.
Which tools to use, and for what
| Category | Examples | Best for | Honest note |
|---|---|---|---|
| General-purpose LLMs | ChatGPT, Claude, Gemini | Drafting, restructuring, devil's-advocate review, call summaries | Fast and flexible, but citations are unreliable and text can feel generic. Best for shaping your own substance. |
| Research-RAG tools | Elicit, Consensus, scite, SciSpace | Literature mapping, real citations, consensus/dissent | Retrieve from real paper corpora, so references are verifiable. Slower; coverage is incomplete. |
| Search-grounded assistants | Perplexity, Gemini with search | Quick sourcing, funder/policy lookups, current information | Good for finding the official call page or policy. Still verify the specific URL it cites. |
| Specialised grant tools | Grantable, Grantboost, and similar | Generating proposal scaffolding from a prompt | Useful for first-draft structure, but output is generic and rarely matches a specific funder's template without heavy editing. |
| Reference managers | Zotero, EndNote, Paperpile | Organising the real literature you verified | Not AI, but the place where AI-suggested references must land once confirmed — your audit trail. |
We teach the full tool list — with free vs. paid trade-offs and institutional data considerations — in the boot camp. You can also request the full itinerary on the contact page.
Funder & publisher rules you can't ignore
Funders and publishers are converging on a simple rule: AI may assist, but a human is responsible for every word, every reference, and every claim. For Horizon Europe and ERC, that means disclosing AI use where the call asks, never fabricating results or sources, and keeping confidential or unpublished material out of tools that train on your input.
- Disclose AI assistance where the funder or publisher requires it.
- Never submit a reference you haven't verified in a real source.
- Don't paste unpublished proposals, partner data, or personal data into public chatbots.
- Use institutional or enterprise accounts where data isn't used for training.
- Keep your audit trail — the prompts, the sources, the human edits.
Five ways AI quietly sinks a proposal
- Fabricated references — every AI-generated citation is a hypothesis until confirmed in a real database.
- Generic prose that reads as AI and weakens your case for novelty.
- Eligibility or scope drift when you trust a summary instead of the official call text.
- Confidential data leaks when unpublished proposals or partner materials go into a public chatbot.
- Over-reliance that leaves you unable to defend the proposal in an interview or rebuttal.
Reading a grant call in nine minutes
One of our most popular demo modules: take a real Horizon Europe call, and use AI to produce, in under ten minutes, a one-page brief covering scope, eligibility, evaluation criteria, key terminology, and the three questions a reviewer will ask first. The point isn't to skip reading the call — it's to read it with a map, so your time goes to the parts that decide whether you're funded.
We run this live in the boot camp on participants' own calls. See the researchers track and research managers track.
Bring the boot camp to your institution.
We run hands-on, customizable training where your researchers and grant officers work on their real calls and drafts — not toy examples.