How dub.mom works, and what it's built on
The main text is written for a reader whose English is a second language. Each section has a denser expander with numbers, sample sizes and caveats. Nothing appears on the landing page that this page cannot back.
The one idea
Reading faster does not come from moving your eyes faster. Eye movement is a small part of reading time. Most of the time goes into recognizing words and putting them together into meaning. [1][2]
So there are only a few ways to read faster that also keep understanding:
- Make the language easier to process: know the words, read in phrases, know the shape of the text.
- Decide what not to read.
- Practice at the edge of your speed, with a check that you understood.
- Slow down for the parts that will hurt you if you get them wrong.
That is the whole program. Every part below is one of those four levers.
Sources and details
Rayner and colleagues' 2016 review is the standard summary. It finds a real trade-off between speed and understanding, and concludes that the way to gain speed without losing comprehension is practice and better language skill, especially vocabulary [2]. Removing the ability to re-read makes comprehension worse [1].
What AI English is, and why it's harder for you
Text from AI tools is not ordinary English. Measured against human writing, it has longer sentences with more clauses, rarer and more formal words, and more padding: it restates the question, adds headings, and ends with caveats. One study put ChatGPT text at a reading grade of about 16.6, against 12.1 for essays by non-native students [21]. And there is a lot of it: in a study of more than half a million conversations between developers and an AI tool, the AI's answers ran about fourteen times longer than the prompts [24].
Two more things make it harder than it looks. AI text sounds confident whether it is right or wrong, so mistakes don't look like mistakes. And people rate AI-written summaries as clear even when they understand them less well than human ones [25]. Feeling fluent is not the same as understanding.
Sources and details
[21] compared 50 student essays with 50 ChatGPT texts on readability and syntactic complexity; it studied essays, not workplace documents, so we treat the exact grade levels as indicative. [24] is a 2026 analysis of 587,568 developer–AI conversations; the ratio is a median. [25] tested 150 people on plain-language summaries. On the "AI overuses these words" point, a 2025 study of 15 million abstracts found 379 words whose frequency jumped once AI writing tools arrived [22]; we use that list, but whether learning it speeds reading is untested — see the third column of the evidence table.
How the program works
Ten minutes a day, one text at a time.
Sources and details
The five L2 speed-reading studies [3–7] are the core. Their weaknesses: the earliest had no control group and did not report comprehension [3]; samples were 29 to 84 students; and part of the measured gain fades after the course ends [4] — which is why dub.mom has checkpoints on matched texts, a four-week check, and a maintenance mode. The "first ten texts" finding is from [3] and should be read as "in that study."
What we measure
What we deliberately keep apart: speed on a text you read yesterday (inflated by repetition) and speed on your own reading with no questions (unchecked). Both are shown, both are labelled, neither goes on your main chart.
Sources and details
The comprehension floor follows Nation's 7-of-10 rule [6]; the 250 wpm "comfortable" mark is his target for second-language readers on known vocabulary [6]; speeds are normalised by characters so texts of different genres compare [26]. The pre/post matching of checkpoint texts follows the correction Gorsuch and Taguchi made to their own earlier work [8].
What's tested, what's mixed, what we're testing
The right-hand column is the honest part. Nobody has studied training people to read AI-generated text. Everything in the program is borrowed from research on ordinary reading, and the borrowing is the bet. Our own study says how we're testing it.
What we don't do, and why
Our own study
The gap in the evidence is transfer: does training on AI-generated text make you faster and safer on the AI text in your own inbox? We are running a pre-registered study to find out: professionals at B1–B2 English, several first languages, four weeks at ten minutes a day, against a group who read the same amount without the program. We measure effective speed on new AI texts, catch rate, transfer to human-written text, and a four-week retest. The plan is registered before the data exists, and the results will be published on this page whether they are good or bad. D
Until then, the landing page makes no claim about results. It says what the program is built on, and it says "we're testing." Findings we do have are on the research notes page.
Sources
Numbered as cited above. Sample sizes and the main weakness of each are given so you can weigh them yourself.
For researchers and L&D
Change log
- 11 September 2026 — Page published.
- Entries added whenever a claim, a number, or a source changes, with what changed and why.