AI Literature Review: Map a Field Without Fake Citations

An AI literature review is a review of existing research in which an assistant helps you plan the search, screen and read sources, extract the key facts into a table and draft the synthesis, while you choose the sources and check every claim. Used that way it saves days on the mechanical parts of a review. Used carelessly it produces something worse than no review: confident paragraphs resting on papers that do not exist, or on real papers that say something different from what is claimed.

This guide is for students, researchers and analysts who need to map a field before writing about it. It sets out a workflow in which the assistant does what it is good at, summarising and organising text you give it, and you keep the decisions that decide whether the review is trustworthy: what to include, what the sources actually say, and what the pattern across them means.

What a literature review is for

A literature review answers the question “what is already known about this topic, how strong is the evidence, and where are the gaps?” It is not a list of summaries. A good one groups sources by theme or argument, compares methods and findings, notes disagreement and ends with a clear statement of what remains unknown. That final step is yours; an assistant that has read only your selection cannot know what the field has missed.

Reviews come in levels of rigour. A narrative review for a course essay or a business brief is flexible. A systematic review follows a fixed protocol, with documented search strings, inclusion rules and screening by more than one person, and it is reported according to standards such as PRISMA. An assistant can support both, but it cannot replace the documented, repeatable steps that make a systematic review systematic.

The one rule: never trust a reference you did not open

Language models are trained to produce plausible text, and a plausible citation has an author, a year, a journal and a title that sound right. Sometimes they are real; sometimes they are assembled from fragments of real ones. This is the known failure described in what are AI hallucinations and how to catch them, and in a literature review it is the one that can end careers.

The safe rule has three parts.

Never copy a reference from an assistant’s memory into your bibliography. Find the paper yourself, in a database or at the publisher.
Give the assistant the paper, not the title. Upload the PDF or paste the abstract and ask questions about that text only.
Check every claim you keep against the paper, at the sentence level, before it goes into your draft.

Research mode helps here because it searches the web and cites its sources, so you can click through to see what was actually said. Cited is not the same as verified, though: open the link, read the passage, and confirm that it supports the sentence. Our guide to researching online with AI and getting cited sources describes how to do that quickly.

AI literature review workflow in seven steps

Step	What you do	What the assistant does	Main risk
1. Question	Decide the exact question and scope	Sharpens wording, suggests sub-questions	A question too broad to finish
2. Search plan	Choose databases and run the searches	Lists synonyms, related terms, search strings	Missing a term used in another discipline
3. Collect	Export results, download papers	Nothing, or de-duplicates a list you paste	Relying on one search engine
4. Screen	Decide in or out by stated criteria	Summarises abstracts, applies your criteria as a first pass	Silent exclusion of good papers
5. Extract	Check every field against the paper	Fills a table from each uploaded paper	Wrong numbers, mixed-up samples
6. Synthesise	Judge the pattern and the gaps	Groups findings by theme, drafts a first version	Smooth prose over weak evidence
7. Check and cite	Verify claims and references	Formats citations from verified details	Invented or mismatched references

Step 1: sharpen the question

Write your question and ask the assistant to challenge it. A useful prompt is: “Here is my review question and the reason I need it. List the ambiguous terms, suggest three narrower versions, and tell me what kinds of study would answer each.” Decide the boundaries before searching: years, populations, countries, study types and languages. Write them down; they become your inclusion and exclusion criteria.

Step 2: build the search

This is a strong use of an assistant such as Ask Mio. Ask for synonyms, alternative spellings, abbreviations and the terms other fields use for the same idea, because a literature in another discipline often has a different vocabulary. Ask it to combine them into search strings with AND and OR, then adapt the syntax to the database you use. Run the searches yourself and record the date, the database and the exact string, so the search can be repeated. Open scholarly indexes such as OpenAlex and the DOI registry Crossref let you check that a paper exists and read its details.

Step 3: collect and de-duplicate

Export references from each database into one reference manager and remove duplicates there. The assistant can help clean a pasted list, but a reference manager does this reliably, and you want the original records, not retyped ones.

Step 4: screen with stated criteria

Paste your criteria and a batch of abstracts, and ask for a table: include, exclude or unsure, with the sentence from the abstract that justifies each decision. Treat this as a first pass that saves reading time, not as the decision. Read every “unsure” and sample the “exclude” group to see whether the assistant is being too strict. For a formal systematic review, human screening decisions must stay human and be recorded as such.

Step 5: extract data into a table

Upload one paper at a time on a plan that includes file uploads, and ask for a fixed set of fields: authors, year, design, sample size, setting, main outcome, key result and limitations, each with the page or section where it appears. Asking for the location is what makes checking fast. The details of working with your own documents are in the AI document analysis guide. Verify the numbers; mix-ups between groups, units and follow-up periods are common.

Step 6: synthesise by theme, not by paper

Give the assistant your verified table and ask it to propose three to five themes with the papers that belong to each. Then write the comparison yourself, or ask for a draft and rewrite it. Ask pointed questions: “Which findings conflict? Which rest on small samples? Which studies use the same dataset, so they are not independent?” The last question matters; counting ten papers that all draw on one survey as ten confirmations is a classic error.

Step 7: check, cite and disclose

Go through the draft sentence by sentence against the extraction table and the papers. Build the bibliography from your reference manager. Then check your institution’s or journal’s rules on disclosing AI assistance; our article on how to cite AI and disclose its use covers common patterns.

Prompts that work for each stage

Mapping a new field

I am new to this topic: [topic]. Explain the main research questions, the common methods and the terms researchers use, in plain language. Then list the types of source I should search for, such as reviews, meta-analyses and datasets. Do not give me specific papers; I will find them myself.

Reading one paper

Using only the attached paper, fill this table: question, design, sample, measures, main findings with numbers, limitations stated by the authors, and the section where each appears. If something is not in the paper, write “not stated”.

Comparing papers

Here are my extraction rows for six papers. Group them by finding, point out where results conflict and explain possible reasons based only on the fields I gave you. Mark any claim that rests on a single study.

All three share the same structure: a limited source, a fixed output and a rule for what to do when the answer is missing. That is the habit that keeps an assistant honest.

What AI is bad at in a review

Judging study quality. It can describe a design, but it cannot reliably tell a well-run trial from a flawed one without the details, and it often defers to confident abstracts.
Completeness. It does not know what it has not seen. A review’s coverage depends on your searches.
Recent work. A model’s training has a cut-off; see why AI misses recent news. Use web search and your databases for current papers.
Numbers inside tables and figures. Values in scanned tables or graphs are easily misread. Check them against the original.
Paywalled papers. It can only work from text you can give it. Do not paste material you are not permitted to share with third parties.

For an overview of reading papers critically with an assistant’s help, see how to read scientific papers with AI without being misled, and for a comparison of tools by task, best AI for research.

Structure of the written review

When the analysis is done, a standard structure keeps the review readable.

Introduction. The question, why it matters and the scope.
Method. Databases, search strings, dates, criteria and how you screened.
Findings by theme. Each section makes a claim, supports it with sources and notes the strength of the evidence.
Discussion. Agreements, conflicts, limitations of the sources and of your own review.
Gaps and next questions. What is unknown or poorly studied.
References. Generated from your reference manager.

Writing the method honestly, including that an AI assistant was used for search suggestions, first-pass screening and drafting, is good practice and often required.

Privacy and data handling

Unpublished manuscripts, peer-review material and data containing personal information should not be pasted into any tool without permission. Read the rules of your institution, funder and journal. Ask Mio does not use chats or files to train models, and you can export or delete your data at any time, but those facts do not replace the permission of the people whose material it is. Our what not to share with AI list is a good checkpoint.

Cost and plans

A normal chat reply costs 1 point and a long one 3 points. Reading one paper and filling a table is usually a long answer, so 20 papers use about 60 points, and a month of regular review work fits comfortably in the Chat plan at €5 a month with 2,000 points, which adds Research mode with web search, file uploads up to 20 MB and memory. The free plan has 600 points a month with Chat and Write only, which is enough to plan a search and refine a question. Prices are in EUR with 21% VAT added at checkout and are taken from the live pricing page.

Frequently Asked Questions

Can AI write a literature review for me?

It can draft one from sources you give it, but it should not choose or invent the sources. Collect papers yourself, extract and verify facts, and have the assistant help group and phrase the findings. You remain responsible for accuracy, interpretation and any disclosure your institution or journal requires.

Why do AI tools invent references?

Language models generate text that looks right, and a citation is a pattern of author, year, journal and title that is easy to imitate. Some invented references are blends of real ones. Never copy a reference from an assistant without finding the actual paper and opening it.

How do I check an AI-found citation?

Search for the title or DOI in a scholarly index or the publisher’s site and confirm the authors, year and journal match. Then open the paper and find the sentence that supports the claim. If you cannot find the paper in an independent database, treat the reference as unreliable and remove it.

Can an assistant do a systematic review?

It can help with search terms, first-pass screening suggestions, extraction and drafting, but a systematic review needs a documented protocol, repeatable searches and human screening decisions, often by two people. Use the assistant to save time inside that process, and report exactly how it was used.

Is it acceptable to use AI for a thesis literature review?

It depends on your institution’s policy, which varies widely. Read the rules, ask your supervisor if they are unclear and disclose your use where required. Using an assistant to plan searches or tidy extraction tables is often treated differently from submitting generated text as your own analysis.

Which Ask Mio mode should I use for this?

Use Research mode for finding web sources with citations, available from the Chat plan, and file uploads for reading and extracting from papers. Use Chat or Write for question refinement, search strings and drafting. The free plan covers Chat and Write, which is enough to plan a review before upgrading.

The Bottom Line

An AI literature review is worth doing when you treat the assistant as a fast reader of the papers you hand it and a good partner for planning and structure, not as a source of knowledge. Find sources yourself, upload them, extract into a fixed table with page references, verify every number and write the synthesis with your own judgement. Start with one small review question and a handful of papers to learn where the assistant helps and where it needs checking. When you are ready for document uploads and cited research, see the Ask Mio plans.

AI Literature Review: Map a Field Before You Write