There are now dozens of AI tools aimed at researchers, and most of them claim to do everything. In practice, each is built around one job: finding papers, mapping a field, reading, keeping track of references or writing. Choosing well means knowing which job you need done.
We make LitraMind, a writing tool, so we're not neutral. We've placed it only where it belongs, in the writing stage, and we name the things it doesn't do. Everything else in this guide is a tool we don't make. Features and prices change often, so we've left prices out and described what each tool is for.
The research workflow, stage by stage
| Stage | Tools | Good for | Watch out for |
|---|---|---|---|
| Finding papers | Google Scholar, Semantic Scholar, Elicit, Consensus | Searching by question rather than keyword; quick orientation | AI search misses many relevant studies (see below) |
| Mapping a field | ResearchRabbit, Connected Papers | Seeing how papers connect; finding work your search missed | Maps follow citations, so very new papers appear late |
| Evaluating papers | scite | Whether later work supports or contradicts a paper | Automatic classification of citation statements can be wrong |
| Reading | NotebookLM, SciSpace, Chat with Files | Asking questions of papers you already have | Summaries drop qualifications; check against the page |
| References | Zotero, Mendeley, EndNote | Storing, tagging and formatting sources | Not AI tools, and that's fine |
| Writing | LitraMind, general chatbots | Outlining, drafting, editing and citing | General chatbots invent references |
1. Finding papers
Google Scholar
Free, enormous and still the baseline. It has no AI synthesis, and its ranking isn't transparent, but it indexes more than almost anything else, including books, theses and preprints. Use it to check whether an AI tool missed something.
Semantic Scholar
A free search engine from the Allen Institute for AI. It adds short machine-generated summaries ("TLDRs"), highlights influential citations and offers research feeds based on papers you save. Many AI research tools build on its open data, so it's often the corpus behind tools you already use.
Elicit
Elicit lets you ask a research question in plain language and returns relevant papers, with extracted details (sample, method, outcome) in a table you can customize. Its strengths are speed and structure: you see what twenty studies did in one view, with quotes from each paper to check against.
Consensus
Consensus is built for yes-or-no questions such as "Does mindfulness reduce anxiety?". It summarizes the papers it retrieves and, for suitable questions, shows a Consensus Meter: how many of the top results lean yes, no or possibly. It's a fast way to see where the literature leans. It's not a meta-analysis: the meter reflects a small sample of results, unweighted by study quality.
The trade-off the comparison tables leave out
AI search tools are usually compared on features. The more important question is what they miss. A 2025 study in Cochrane Evidence Synthesis and Methods by Lau and Golder compared Elicit's results against four published evidence syntheses.
- 39.5%Average share of the reviews' included studies that Elicit found
- 94.5%Share found by the original, traditional database searches
- 41.8%Elicit's precision: share of its results that were relevant
- 7.6%Precision of the traditional searches
Put those numbers on a review with 100 relevant studies. A traditional search finds about 95 of them, but to get there you screen roughly 1,250 records. Elicit finds about 40, and you screen fewer than 100. That's around thirteen times less screening, and 60% of the relevant studies missing.
Both numbers matter. For getting oriented in an unfamiliar topic, high precision is exactly what you want: a short list of relevant papers, fast. For a systematic review, or any claim about "the literature", missing more than half the evidence isn't acceptable. The authors' conclusion was that Elicit isn't yet sensitive enough to replace traditional searching, though it found some studies the original searches had missed. Use AI search to start and to supplement, never as your only search.
2. Mapping a field
ResearchRabbit and Connected Papers start from one or more papers you already know and draw the papers around them as a graph, based on citations and similarity. They're particularly good at two things: finding the foundational papers everyone in a field cites, and finding related work that uses different vocabulary from yours and never came up in keyword searches.
Because they work from citations, new papers that haven't been cited yet are underrepresented. Pair them with a date-sorted database search.
3. Evaluating papers
scite looks at the sentences in which later papers cite a given paper and classifies each as supporting, contrasting or simply mentioning it. Before you build on a striking result, it's a quick way to see whether others replicated it or challenged it. The classification is automatic, so read the contrasting statements yourself before drawing conclusions.
Before citing anything, also check that it hasn't been retracted. Our guide to checking every reference covers how.
4. Reading
NotebookLM
Google's NotebookLM answers questions using only the documents you add to a notebook, with inline citations to the passages it used. Limiting it to your sources makes it far less likely to make things up, and it says when your sources don't cover a question. It can't search the literature for you: it only knows what you give it.
SciSpace
SciSpace explains papers as you read them, including equations, tables and unfamiliar terms. It's useful when you're reading outside your field. As with any summary, check the explanation against the text before you rely on it.
Chat with Files in LitraMind
Chat with Files lets you ask questions of a PDF or Word document and get answers with page references, inside the same workspace where you're writing. If you're new to reading papers efficiently, our guide on how to read a research paper explains where AI helps and where it doesn't.
5. Managing references
The reference manager is the one part of the stack where AI matters least. Zotero is free and open source, saves sources from your browser in one click, and includes 300 MB of free cloud storage for attachments. Mendeley and EndNote are the main alternatives, and many universities provide EndNote. What matters is that every source lives in one library with clean metadata, so your citations are correct and you can switch writing tools without losing anything.
6. Writing
General chatbots
ChatGPT, Claude and Gemini are flexible and useful for explaining concepts, brainstorming and editing your own text. They're unreliable for one specific, important job: references. Asked for sources directly, they can produce citations to papers that don't exist. If you use them, never take a reference from them without finding the paper yourself.
LitraMind
LitraMind is a workspace for academic writing. You can build an outline with Write New Paper, draft with AI Write using your own uploaded papers as context, and insert citations with Research & Cite, which takes them from real scholarly records in your chosen style. AI Edit then suggests improvements one at a time.
What it doesn't do: it's not a replacement for a systematic database search, and it doesn't replace your reference manager's library. It's where the writing happens.
Three setups for different work
| If you're… | A sensible setup |
|---|---|
| A student writing an essay or dissertation on a budget | Google Scholar and Semantic Scholar to find papers, Zotero for references, NotebookLM or Chat with Files for reading, and a writing tool with a free tier |
| A PhD researcher doing a literature review | Database searches plus Elicit to scope the topic, ResearchRabbit to fill gaps, scite before building on key results, Zotero, and a writing workspace that inserts citations from records |
| Running a systematic review | A documented search in the databases your field expects (PubMed, Scopus, Web of Science), with AI search only as a supplementary, reported method; dedicated screening software; and a reference manager for deduplication |
How to judge any AI research tool
New tools appear every month. Six questions sort them quickly:
- Where does its information come from? A named scholarly database, your own documents, or the model's memory? Only the first two are suitable for citing.
- Can you check every claim? Look for links or page references to the exact source passage.
- Is it reproducible? Could you run the same search again and report it in a methods section?
- What happens to your uploads? Read the data policy before uploading unpublished work.
- Does it export cleanly? References should move to your reference manager without retyping.
- What's independent evidence on its accuracy? Vendor benchmarks are a start, not an answer.
The bottom line
The best AI research setup is a small number of tools, each used for what it does well, with a human checking the joins. Use AI to search faster, read faster and write faster, and keep the judgment about what the evidence says for yourself. If you're bringing these tools into published work, see what journals expect you to disclose.
