Research literacy is the ability to find what a paper actually claims, how it tested that claim, and where the evidence stops. Read the abstract and figures for a map, inspect the method and comparison, then write a short explanation with a reproducible follow-up question.
Read a figure together with its axes and evaluation setup.
Find a baseline and a limitation.
Write a source-linked research note in your own words.
[ READ · TRY · CHECK ]
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01
LESSON 01 / 03
Find the central claim
Identify the question, proposed contribution, and conditions attached to the result.
The idea
A paper’s claim has a task, comparison, metric, and conditions.
Read the title and abstract to locate the question, then restate the main claim in your own words. Add the evaluation setting: what task, what data, which baseline, and what measure? “Better” is incomplete without these conditions.
The abstract is a map, not the full evidence. Use figures and tables to decide which sections deserve close reading.
WORKED EXAMPLE
Turn a headline into a checkable claim
Instead of “model A is better”, write “model A scored higher than baseline B on dataset D using metric M under this compute budget.”
YOUR TURN
What is missing from “our method beats previous work”?
Need a hint?
Look for the comparison and measurement.
Check your answer
The specific baseline, task or dataset, metric, and conditions are missing.
Try it further: Does “better than baseline” specify which baseline and on what task?
02
LESSON 02 / 03
Inspect the evidence
Read tables, metrics, datasets, and ablations before accepting the headline.
The idea
Evidence depends on how the comparison was made.
Inspect the dataset split, baseline implementations, metrics, sample size, and resource budget. Read axes and footnotes before repeating a chart. An ablation can show whether a claimed component contributed to the result.
A gain on one benchmark may not transfer to another. A small improvement can also cost much more compute or latency. Write both quality and cost beside a reported result.
WORKED EXAMPLE
Question a table
If accuracy rises one point while inference cost triples, the trade-off depends on the application and the uncertainty of the measured gain.
YOUR TURN
Why is an ablation useful?
Need a hint?
What changes when you remove one part while keeping the rest similar?
Check your answer
It helps test whether that part caused the improvement, though the conclusion still depends on the experimental setup.
Try it further: What would you ask if a model improves one metric but uses much more compute?
03
LESSON 03 / 03
Write a useful research note
Summarise the result, caveat, and one experiment that could challenge it.
The idea
A research note should let another reader verify and challenge you.
Link the original paper. In a few sentences, state the question, method, strongest result with its metric, a limitation, and a follow-up experiment. Separate what the authors measured from your own inference.
Choose a follow-up that changes one factor, such as a new dataset or resource budget. If you cannot describe how it would change the conclusion, the note may still be too vague.
WORKED EXAMPLE
Five-part paper note
Question → method → measured result → limitation → one testable next step.
YOUR TURN
What should you do if a paper reports a number without enough detail to reproduce it?
Need a hint?
Avoid presenting uncertainty as certainty.
Check your answer
Record the missing setup, qualify the claim, and look for code, appendices, or another reproducible comparison.
Try it further: How would you explain a paper without repeating its abstract?
[ PUT IT TO WORK ]
Make a one-page paper briefing
Choose an openly available AI paper. Record the original link, question, method, strongest evidence, largest caveat, and a small reproduction idea.
Check your work
The note links to the original paper.
A reported number includes its metric and dataset.
The limitation and follow-up are specific.
[ GO DEEPER / FOLLOW THE SOURCE ]
Further reading
These sources support further study. The lessons above are original AI Ka School material.