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How to use AI for your literature review in 2024

ChatGPT has a bad reputation for finding literature, but only if you misuse it. In this workflow, we will collect many papers with an AI-powered tool and then filter them all simultaneously using ChatGPT and Zotero. You are guaranteed a truthful reply from the AI and yet can process 100s of papers in seconds.

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The outline of our workflow looks like this:

  1. Collect relevant papers using AI tools (Litmaps, SciSpace, Consensus) or manually. If you have a collection already, skip this step.
  2. Use your reference manager(Zotero, Paperpile, Endnote etc) to export all papers and abstracts.
  3. Use ChatGPT (and ideally a custom GPT bot) to identify important papers
  4. Read and annotate papers using Obsidian.

1. Collecting Papers with Litmaps, SciSpace or Consensus

Currently there are dozens of tools you can use to discover new literature. There are essentially three types of tools that we can use:

  • Keyword-based tools: Using words to find papers by title and abstract (e.g., Google Scholar, Pubmed, etc.).
  • Citation-based tools: Starting from a “seed” set of papers (even just a single one), we discover other papers referencing or referenced by the seed set. (Litmaps)
  • AI-based tools: Your query is semantically compared by an AI to Millions of abstracts to find the right papers.

AI-based tools are in vogue right now, but their problem is that they might miss important papers. Keyword-based tools do not have this problem but often return overwhelming results. The sweet spot seems to be finding a set of seed papers using AI-based tools and then exploring its citation network using citation-based tools.

1.1 Collecting initial seed papers with SciSpace

SciSpace is an excellent AI based tool for the job. Consensus is similar to that but works better for medical questions. It always starts with a search box where you type in your research question. Note that you do not start with keywords but with a question.

In the case of SciSpace, the result is a set of papers displayed as a table. You can have the AI analyze any particular aspect of it. For example, suppose you are an ecologist interested in the forests of northern China. In that case, you can create a new “column” and ask the AI to analyze each paper and assess whether the study area is indeed north China. This is impossible to do reliably with a keyword-only search.

Here are the predefined columns SciSpace suggests (You can add custom ones in the tool as well):

Once you have collected a set of papers that seem relevant, do not be overly critical of which ones you pick. In step 3, ChatGPT will filter through all of them and choose the most relevant ones regardless of how many papers you collect.

The easiest way to collect papers is by using the Zotero Connector. It is a Chrome browser plugin that allows you to import bulk papers from most pages and apps.

1.2 Grow your collection with Litmaps

Litmaps is a tool that you can use to explore the citation network. It is the most usable and valuable tool for a lit review and, even as a student, worth the 10$/month investment. You can use it for free if you are dealing with a small collection of 25 papers or less.

First, you are going to export the papers in your Zotero collection to Litmaps:

From here, you can discover “related articles” connected by the citation network. Litmaps arranges papers on a 2D map. At the top are papers with more citations, and to the right are recent papers. Publication date and citations are negatively correlated; your papers are roughly arranged along a diagonal (top left to bottom right). Pick the papers at the top right for the most impactful and recent papers.

This process is explained in breadth as part of the Effortless AI Literature Review course.

Keep collecting papers and adding them to Zotero as you find interesting ones.

At the end of this fun process, you might end up with 100s of papers. Before AI, this would have been a giant problem because nobody had time to read so much. Even skimming would be critical. In the Effortless Literature Review 1.0, we would use a filtering system to pick recent and well-cited reviews. However, we can automate this step and add much more value to it using AI!

2. Creating a file with all abstracts to use with AI

To have AI look at our entire collection and help us decide what is relevant, we need to export everything we have collected into a readable format. Every reference manager can export to “BibTeX,” a universal citation format. In most cases, it will contain abstracts of the papers as well. Here is how to do it:

The resulting file has an “*.bib” extension and will not be readily read by ChatGPT or your system. Renaming it to txt will reveal its contents:

You can use any reference manager for this step since they all support an export to BibTeX, and the format looks the same. However, sometimes Zotero will not be able to retrieve the abstract of a paper and ChatGPT won’t be able to use it either. You can find out if you look into the file. Each paper begins with @article or @techreport, followed by a few properties. If the “abstract” property is missing you can either add it manually or just keep in mind that this paper will be less likely to resurface in your AI analysis.

3. Use ChatGPT to create a thought out reading list

The final step is to upload all these abstracts to ChatGPT and ask it to create a reading list concerning our research question. The more clearly and detailed you define your research question, the better AI replies.

Upload your bib/txt file and use the following prompt:

My goal is to learn how {insert your goal here}.
1.  Analyze the papers mentioned in the document. Use only them and nothing else. 
2.  Create a reading list that takes me from novice to advanced. Starting with broader papers and ending with very specific papers.
3.  For each paper identify 2 questions as my "learning goal" based on what you infer in the abstract. 
Choose only 5{insert your number here} papers from the collection, as I do not have more time.

ChatGPTs responses will be stochastic so it might be worth to run the prompt a few times and see which papers come up multiple times to be sure.

The most important part of this prompt is to provide you with a list of “learning goals.” Look at all learning goals and assess how they relate to your research question. Does it answer it?

If you feel something is missing just ask ChatGPT a follow up question: “What paper is likely to answer {insert your question/goal}”. Given so many abstracts on a topic ChatGPT became somewhat of an expert on your field.

Instead of writing prompts and uploading files you should create a GPT bot that will contain all information and instructions. You can design your bot to answer any questions regarding this literature review to really supercharge your workflow. This course will teach you exactly how:

Achieve 10x efficiency in research, learning, and everyday tasks with ChatGPT bots. This course takes you from knowing nothing to using the most cutting-edge techniques most people don’t know about in just a few hours.

4. Do not skip reading

The last part is to read your papers. No amount of AI will save you from critically reading your papers. But now you have only a small set of documents to read and won’t be overwhelmed. Read 1-2 papers a day, but read deeply. Here are eight tips on becoming the best read person.

When you read your main challenge is to take efficient notes that you will be able to:

  • Easily find,
  • quickly synthesize,
  • interconnect,
  • and grow over time

The best software to use here is Obsidian. I have a free 8-day course to learn the basics and an in-depth course to make you a note-taking expert in academic matters. Obsidian is to knowledge what Google Maps is to navigation. It is the perfect tool for academics dealing with a lot of information.

Here are some more reasons to use Obsidian in academia.


You learned how to find a few papers using AI-powered tools (Consensus, SciSpace) or even Google Scholar in this workflow. We collected all the documents using Zotero or any other reference manager.

Then, we explored the citation network with Litmaps to find as many papers as we wanted without being overly picky.

Lastly we used ChatGPT to do the hard work of skimming all the abstracts for us.

The end result is a well curated list of the most relevant papers tailored to your exact research question with a set of learning goals per paper to guide your lit review.

From here on you can enjoy reading about your topic and learning what it is.