Conceptual Literature Review

Dear Reader,

A literature review starts with reading. But what is reading other than unpacking packaged information into a set of ideas that link together in meaningful ways in your head?

What if we could start the process with the mindmap?

This is what I call a conceptual literature review. Given a few papers, it uses AI to extract concepts and claims and create an interlinked information network. The output is an Obsidian vault with 100s of connections and dozens of notes:

Notice a list of concepts in the sidebar; One of these concept notes is open on the top-right. It mainly consists of claims extracted from the input papers. Each claim is an atomic sentence, which can be used in writing (see recent academic writing webinar). Each paper is additionally transformed into a source note with key concepts and contributions (bottom right).

This mind map makes learning much faster and more "semantic". A meta-analysis found that these mind maps, even when created by others, improve recall of the subject (Nesbit & Adesope, 2006).

How is this different from an AI summary? If an AI summary is a low-resolution satellite image, the conceptual lit review is a map with precise annotations, street names, and the ability to zoom in on every detail. They use the same underlying information, but the latter is more precise, schematic, and useful.

I found this approach a genuinely novel way of diving into a topic, which would not have been even remotely possible before AI. Give it a try if you are in the midst of learning new concepts or download the demo vault, which I generated with this method.

Summary: This workflow takes review papers and uses an AI skill to extract the authors' claims, grouped by the concepts they relate to. This information is then written into a set of interconnected concept notes. The result is a Wikipedia-like information network (accessed via Obsidian), which greatly helps you understand the subject matter. The article provides background on why this works and the AI skill you'd need to reproduce the results.

Soon: Knowledge Management Course and Cohort

For the past month, I have been recording an entirely new note-taking & knowledge management course. It is the culmination of over 4 years of experiments with knowledge management in academia. Especially since the introduction of bases, the way we use Obsidian has changed, and the new course captures that.

You will learn how to organise and synthesise the exploding amount of information in academia in a way that makes you more productive without any hacks. Just systems. This is like riding a bicycle; you're faster without any external fuel, as long as there's a road. The information management system in this course is exactly that road.

(And of course, if you're envious of the view, come to Wellington, where almost every house has one, if the wind has blown it off yet. Those who know, know what I am talking about...)

21-day Cohort or Self-Paced Course?

There will be two options to join the new course. First, as an intense 21-day cohort of just a handful of people, which I will personally guide in numerous small-group sessions. Participants will have access to all recordings and course videos. After the feedback and success of the last cohort in February (see testimonials there), I know how to make this one even better. Since spaces will be limited, you need to pre-register for the waitlist to join:

The second option will be the pre-recorded course. The content is arranged in small digestible chunks with a lot of detailed annotations and homework. I will be working closely with the first people to join the course to iron out the kinks, so if you're keen for some additional support from me, make sure to join the waitlist for the course as well:

Do you like the idea of the conceptual lit-review, or not your pair of shoes?

Wishing you a wonderful weekend,

​
Ilya Shabanov
The Effortless Academic​

This is the weekly Effortless Academic Newsletter. It consists of an in-depth tutorial and additional events, promotions, or relevant information for the AI-curious modern academic. I strive to consistently provide value with every email. If this is not relevant to you, you can always unsubscribe from everything and will never hear from me again. If you find the email somehow inappropriate, please reply to me and let me know what you didn't like.