AIMPAPERS
AI for research and writing — tools and guides for smarter papers, citations, and document workflows.
From converting words to pages and scoring readability to budgeting AI token costs, planning a draft, and checking how tightly a summary compresses, AimPapers pairs free hands-on tools with honest, practical guides on using AI for research and writing. Draft smarter, cite carefully, verify everything.
Research & Writing Tools
Free tools for research and document work — word-count to page-count converter, reading-time estimator, Flesch readability scorer, AI token & cost estimator, writing-time planner, and summary compression-ratio calculator.
Word Count to Page Count Converter
Turn a word count into an estimated page count (or the reverse) for a standard 12pt page, single or double spaced.
Reading Time Estimator
See how long a text takes to read silently and to read aloud, from its word count and an adjustable reading speed.
Flesch Readability Scorer
Score a passage with the Flesch Reading Ease and Flesch–Kincaid grade formulas, with the counts behind them.
AI Token & Cost Estimator
Convert prompt and response word counts into estimated tokens and dollars, using separate input and output rates.
Writing Time Planner
Estimate how long a draft will take from a target word count and an honest drafting pace, day by day.
Summary Compression Ratio Calculator
Compare a summary with its source to see the compression ratio, the reduction, and how many times shorter it is.
Museum of Writing & Knowledge
Wander an immersive, richly illustrated collection — the history of writing and scholarship, the great libraries and archives, the rise of artificial intelligence, and the printing and publishing that spread the written word, each in its own gallery. Every entry, image, and fact is drawn from Wikipedia and Wikimedia Commons.
Tour the museum →From the Journal
Honest, practical guides on using AI for research and writing — drafting without cheating, checking AI citations, prompt patterns, summarizing long papers, readability, managing references, and the limits of AI — reading to pair with the tools above.
Why AI Fabricates Citations (and How to Verify Every One)
A fabricated citation looks exactly like a real one: same format, same confident tone, same plausible journal name. Understanding why models invent sources — and what real verification actually costs in time — is what keeps a fake reference out of your bibliography.
What an AI Research Workflow Actually Costs in Tokens
Providers bill by the token, not by the request, and input and output are priced separately. Here is how to turn a realistic writing workflow into an actual token-and-cost estimate — using your own numbers, not a headline price that will be out of date by the time you read this.
What Readability Scores Actually Measure — and What They Miss
A Flesch score is precisely defined arithmetic on sentence length and syllable count, nothing more. Run it on three real passages and the numbers make the formula's real behavior obvious — including exactly where it stops being useful advice.
Words to Pages, Honestly: Why the Question Is Underspecified
"How many pages is 2,500 words?" has no single answer until you say what font, spacing, and margins you mean. Here is the actual arithmetic behind a page-count estimate, worked both directions, and why every number it gives you is a starting point rather than a promise.
Planning Writing Time Realistically (Not Optimistically)
Typing speed and writing speed are different numbers, and the gap between them is where most schedule failures come from. Here is the arithmetic for turning a word-count target into an honest hours-and-days plan — worked at three different paces so you can see how much the assumption matters.
Summarization Ratios: What a Compression Number Actually Tells You
A summary that keeps 8% of a source's words is a fundamentally different object from one that keeps 50%. Working the actual ratios for a few compression levels makes it obvious what kind of loss is happening at each one — and why a smooth read is not evidence that nothing important was cut.
Keep Learning
Wiki-sourced explainers on the subjects behind the work — artificial intelligence and language, research and writing, citations and academic integrity, and the libraries and knowledge that hold it all together.
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