AI research assistants have changed how a lot of people start a literature review. You can describe a question in plain language, get a list of relevant papers, and see short summaries of each before you have finished your coffee. For anyone who used to spend the first day of a project typing keyword variations into a database, that is a real gain.
The risk is that the speed carries straight through to the draft. A summary reads well, the citation looks right, and it goes into the report without anyone opening the paper. Most of the time that is fine. The few times it is not are the ones that hurt: a claim the paper does not actually make, a finding from a small pilot presented as settled, or occasionally a reference that does not exist at all, which is a known failure mode of general-purpose chatbots that are not grounded in a paper database.
None of this is a reason to stop using these tools. It is a reason to put a short, consistent check between "the assistant found it" and "it is in my document." This is the routine we suggest, and it takes about fifteen minutes for a typical set of sources.
Step 1: Confirm the source exists and is what it claims to be
For every paper you plan to cite, open the original record: the publisher page, a DOI link, or a recognised index. Check that the title, authors, year and venue match what the assistant gave you.
This sounds trivial, and it is meant to be. It takes seconds per source and catches the most embarrassing class of error. Tools built specifically for research, which search an underlying academic database and link each result to its record, make this step much faster than a general chatbot does.
Step 2: Read the abstract and find the sentence
Next, find the exact sentence or table in the paper that supports the claim you want to make. If you are using an assistant that quotes or highlights supporting passages, use that as a pointer, not as the final word. Go to the passage yourself and read the surrounding paragraph.
What you are checking for:
- Direction. Does the paper support the claim, or does it report a mixed or opposite result?
- Scope. Is the finding about the population, setting or time period you are writing about?
- Strength. Is it a primary result, a secondary analysis, or a line in the discussion section about what might be true?
A surprising number of problems appear here. Summaries tend to compress hedged language, so "may be associated with in this sample" can become "causes" by the time it reaches a bullet point.
Step 3: Check the study type and size
Not every paper deserves the same weight. A large randomised trial, a systematic review, a small pilot study and a preprint that has not yet been peer reviewed all carry different levels of confidence.
Many research assistants can extract study details into a table, such as sample size, design and outcomes. That is one of their most useful features, because it lets you compare sources side by side. Spot-check a few of those cells against the paper itself; extraction is usually good, but it is not perfect, especially for numbers buried in tables.
Step 4: Look for what the assistant did not show you
Search results are shaped by the question you ask. If you only ask for evidence that something works, you will mostly get that. Run one more query framed the other way: limitations, null results, criticism, or replication attempts. If the picture changes, your draft should reflect it.
Step 5: Record what you checked
Keep a simple log next to your reference list with three columns: source, the claim it supports, and the passage or page where you found it. It takes a minute per source and saves a great deal of time when a reviewer, editor or colleague asks "where does this come from?"
It also makes your work reusable. The next person on the project can trust the citations without redoing the check.
Choosing a tool that makes this easier
If you are comparing research assistants, the features that matter most for this routine are:
- Grounding in a real paper database, so every result links to an actual record.
- Visible supporting passages, so you can jump from a summary to the text behind it.
- Structured extraction, so you can compare study design and size across papers.
- Export, so citations move cleanly into your reference manager.
Elicit is one example built around searching papers and extracting study details into tables, and there are several others with different strengths. The AI research assistant category on Toolbit lists options side by side if you want to build a shortlist and test them on a question you already know the answer to. That last part is the best evaluation there is: pick a topic where you know the literature well, and see which tool finds what you would have found, and flags what you would have missed.
The assistant does the searching. You still do the reading. Fifteen minutes of checking is what keeps the speed from turning into a liability.