Yes, you can learn far more than most people assume, but not anything to any level on any timeline. In a large study of students practicing in real courses, they started far apart yet improved at a strikingly similar rate with each attempt that came with feedback. Whether you can learn anything depends mostly on what you already know, how many good attempts you make and time, though a few limits are real, such as native-like grammar in a language you start as an adult.
This guide answers whether anyone can learn anything, using research on learning rate, prior knowledge and practice, and turns the answer into a one-week plan for learning one idea from one source.
A student on r/PhysicsStudents asked what most people only ask themselves: "I want to study sciences and maths, but I’m scared I’m not smart enough, should I still try?" (thread). The fear usually comes from a comparison: someone in your class reads the chapter once and seems to have it, while you read it twice and still can't explain it. That looks like a difference in ability. More often it is two other differences: where each of you started, and how many real attempts each of you has made since.
Can anyone learn anything, or do some people just learn faster?
Some people start ahead. The gap in how much each attempt teaches looks smaller than most people think.
In 2023, Kenneth Koedinger, Paulo Carvalho, Ran Liu and Elizabeth McLaughlin analyzed 1.3 million answers from nearly 7,000 students in 27 datasets of online practice in math, science and language courses, from elementary school to college. Each answer was one attempt at one small skill, such as a type of fraction problem or the use of an English article, and a wrong answer brought feedback and help.
Before any practice, after the course's lectures and readings, students got about 65% of first attempts right, and the spread was wide: about 55% for the lower half of a class, 75% for the upper half. Then the authors measured how much each attempt improved each student, and here the spread almost disappeared. Each attempt added roughly 2.5% in accuracy, so from about 65% a typical student needed about seven attempts to master a typical skill, with mastery set at 80% correct.
So the classmate who gets it in one reading usually isn't learning faster than you. They started closer to the finish. The authors concluded that, given favorable conditions and enough practice opportunities, "anyone can learn anything they want", and in the next sentence framed it as a hypothesis: "If true".
Two cautions apply. The students had met their courses' prerequisites and practiced with software that gave feedback on every attempt, so the finding is about learning under those conditions. And the result is disputed. In 2026, Hansol Lee and colleagues ran the same analysis on the same 27 datasets but counted more or less of each student's practice. The gap between students in starting knowledge stayed about the same, but the estimated gap between them in learning rate came out more than twice as large under one choice and several times as large under the other. So the main finding holds for starting points; how alike people are in learning rate is less settled. Their paper is a preprint, not yet peer-reviewed.
The safe reading: differences in where people start are large and well measured. Differences in how much each good attempt teaches are smaller than most people assume, and how small is still argued.
What to take from it: count attempts with feedback, not hours or how clever you feel.
Why does the same chapter feel easy to one person and impossible to another?
Because a chapter is written for a reader who already knows certain things, and that reader gets most of it for free.
Donna Recht and Lauren Leslie (1988) showed how much this matters with 64 junior high students, sorted two ways: strong or weak readers, and knowing a lot or a little about baseball. Everyone read the same account of half an inning, then reenacted it on a model field, retold it and summarized it. Knowledge of baseball had a significant effect on every measure, and the weak readers who knew the game outperformed the strong readers who didn't. It is one small study on one topic, but it lines up with the 2023 data: what you already know decides where you start.
Understanding a new idea means connecting it to ideas you already have. A sentence like "the reaction is exothermic, so heating it shifts the equilibrium to the left" is easy if you know what exothermic and equilibrium mean, and close to noise if you don't. When a chapter assumes ideas you never learned, everything after them feels hard, and from the inside that feels like the ceiling of your ability. Usually it is a missing floor.
Try it now: write down three things your next chapter assumes you know, and explain each in two sentences without looking. If one fails, that is where this week's plan starts, one level down.
What to take from it: before you blame your ability, check the floor the material stands on.
Ritsu is an AI tutor built around that floor and those attempts: it maps what a source assumes, asks you to answer before it explains, and brings each idea back later. The last part of this page runs the week's plan in it. Start Learning Free
Is practice enough to get good at anything?
Practice is the part you control, but not the whole story. Brooke Macnamara, David Hambrick and Frederick Oswald (2014) pooled the studies that measured deliberate practice, structured activity designed to improve performance, and asked how much of the difference between people it explained. The answer depended on the field: 26% of the variance in performance in games, 21% in music, 18% in sports, 4% in education and less than 1% in professions.
That seems to contradict the 2023 finding, but the two studies answer different questions. Macnamara's compared people who had reached different levels, usually by estimating how many hours of practice they had put in over years. Koedinger's counted single attempts at small skills, each followed by feedback, inside one course. Hours are blunt: an hour of rereading and an hour of answering and fixing mistakes count the same.
Read together, they say something narrower and more useful. Years of practice don't explain most of the difference between performers, but each attempt that comes with feedback moves a learner forward by a similar step, and the number of those attempts is something you can plan.
So "I'm not a math person" is, more often than it feels, a statement about where you started and how many good attempts you have had. You can't change the first. The second is a schedule.
What to take from it: plan attempts, not hours.
What does it take to truly learn something?
It takes being able to do something with the idea when the page is gone. Recognizing an explanation and producing one are different achievements: you can follow every line, pick the right option in a multiple-choice question, and still lose the thread when someone asks why it works.
Michelene Chi and colleagues (1989) watched that difference as students thought aloud while studying worked examples of mechanics problems. The students who later solved problems best kept explaining the examples to themselves: why each step followed, which principle from the text it used. The weaker students produced few explanations, misjudged what they understood, and leaned on the examples when they had to solve problems alone.
The paired video turns this into five jobs, a useful map for any source:
- Structure. What is the source about, and which ideas connect?
- Find what matters. Small ideas, each with one clear question.
- A thinking action. Answer, explain, compare, predict or solve, without looking.
- Feedback and evidence. Find what your answer missed and change the next attempt.
- Return. Test it again later; what you understood once may be gone next week.
The episode presents the five as a practical design, not a formula tested on everyone, which is the right status for them. Each job has research behind it; the five together are a plan, not a finding. The Feynman technique is one way to run jobs three and four on a single idea.
How do you learn one new thing in a week?
Here are the five jobs as a week, for anyone planning on a Sunday evening. Pick one source you need: a chapter, a lecture, a long article. Pick one goal you can check, such as "explain why built-up neighborhoods stay warm after sunset and predict what changes it". The plan takes about two hours this week; the reviews carry on after it.
Sunday evening: map the source and what it assumes (20 minutes)
Skim the headings, bold terms and figures, and write the main ideas as five to eight questions in the order they build: your structure and your list of what matters. Then write what the source assumes you know and test each assumption in two sentences, page closed. Any that fail go to the top of Monday.
Done: one page of questions in order, missing basics first.
In Ritsu: upload the source and it comes back as an ordered path of small units, each traced to the page it came from, and /prereq lists what a concept stands on, from foundational to advanced.
Monday to Wednesday: answer before you read (25 minutes a day)
Take two or three questions a day. For each, read the section once, close it, and write your answer in full sentences. Then open the source, mark what your answer missed, and correct it in your own words. That is one practice attempt with feedback, and a wrong first answer is the useful kind: it shows you what to fix.
Plan several attempts per idea across the three days and the reviews after them, not one perfect session; in the 2023 data a typical skill took about seven. Vary the question: why does it happen, how does it differ from its neighbor, what would change it? This is active recall, done before the explanation as well as after.
Done: each question has a written answer, a correction and one line on what you missed.
In Ritsu: /askme puts questions to you one at a time about the idea you are on, and evaluates each answer with feedback before you move to the next.
Thursday: change the case (20 minutes)
Use each idea on a case the source didn't show, and mix the ideas instead of taking them in order: interleaving makes you choose which idea applies, which is what a test asks. An answer that only works on the source's own example is still the source's answer.
Done: every idea has survived one new case, or you know where it broke.
In Ritsu: write your own prediction first, then use /what-if to trace the consequences of the changed condition step by step and compare.
Saturday: return without the source (15 minutes)
Answer every question from Sunday's list from memory: write first, check second. This is a mastery check, a short test you run on yourself before moving on. It doesn't ask "Did I finish?" but "Can I retrieve, explain or apply it without my notes?" What you miss goes on next week's list; what you get right should come back in a few days and again in a week, the job spaced repetition does with software.
Done: you know which ideas are yours and which go on next week's list.
In Ritsu: add the session to Review and it comes back after one day, then three, seven, 16 and 35, built from your own file, with no cards to make.
| Day | Job | What you do | Done when |
|---|---|---|---|
| Sunday evening | Structure, find what matters | Write the source's ideas as questions and test what it assumes | One page of questions, missing basics first |
| Monday to Wednesday | A thinking action, feedback | Answer before you read, then correct the answer | Every question answered and corrected |
| Thursday | Feedback and evidence | Use each idea on a new case | Each idea has survived one new case |
| Saturday | Return | Answer every question from memory | You know what is yours and what goes on next week's list |
What does one honest attempt look like?
Here is the experiment from the paired video, on a short passage written for it. Read it once, scroll until it is out of sight, and answer the question below in writing. Take two minutes. Don't try to sound impressive; try to be specific.
Why Can a City Stay Warm After Sunset?
In many cities, summer evenings feel warmer than in nearby less-developed areas. This pattern is called an urban heat island. It does not mean that every city is hotter every night or that every neighborhood has the same temperature. A heat island is a local temperature difference shaped by the built environment, surrounding landscape and weather.
Consider what happens when a patch of trees and soil becomes a parking lot and several buildings. The new pavement usually offers less shade and less evaporative cooling than plants and moist ground. Many dark urban materials absorb substantial sunlight during the day and release part of that stored heat after sunset. At the same time, building arrangements can change airflow and the exchange of heat with the sky; vehicles and air conditioners can contribute waste heat. These processes can reinforce one another, but their importance differs across locations.
The question: Why might a built-up neighborhood remain warmer than a nearby vegetated one after sunset? Describe two processes, and name one condition that could change the difference.
A first attempt. A rushed answer sounds like the one in the video: "The city stays hot because there's more concrete." It blames the right thing, but it can't answer the follow-ups. What does more concrete actually do? Why does the time after sunset matter? How would trees change it?
The repair. Go back to the passage only for those three gaps. Dark urban surfaces absorb sunlight during the day and release part of that stored heat after sunset. Pavement offers less shade and less evaporative cooling than plants and moist ground. And the size of the difference depends on conditions such as the weather, the layout of the streets and how the temperature is measured. The U.S. Environmental Protection Agency's page on heat islands describes the same mechanism: heat islands "become more pronounced after sunset" because urban materials release their heat slowly.
Now grade your own attempt. Did it name two processes, such as heat stored by day and released at night, and less shade and evaporative cooling? Did it name one condition that changes the gap, such as weather, street layout or how temperature is measured? Whatever is missing is your next attempt's target.
The second answer isn't better because it is longer. It names the link the first one skipped, that concrete stores heat by day and gives it back at night, and it says where the explanation stops. That repair, a missing connection found and fixed, is one practice attempt with feedback: the unit the 2023 study counted.
Thursday's new case could be two streets with the same pavement, one lined with tall buildings that block the wind: which cools faster at night, and what else would you need to know? On Saturday, write the explanation again with the passage closed.
What mistakes stall a learning week, and where are the real limits?
The plan fails in predictable ways:
- Counting hours. Two hours of rereading is zero attempts. Count the questions you answered and corrected.
- Reading the explanation first, every time. You will recognize everything and produce nothing. Answer first, even badly.
- Skipping Saturday. Without the return, you can't tell understanding from familiarity.
And "anyone can learn anything" has real edges:
- Native-like grammar in a new language. Using data from nearly 670,000 English speakers, Joshua Hartshorne, Joshua Tenenbaum and Steven Pinker (2018) estimated that the ability to learn a language's grammar stays high until about 17 and then declines steadily. Adults still learn languages; reaching native-like grammar is the part that gets harder.
- Elite performance. In the 2014 meta-analysis, practice explained about a quarter or less of the differences in games, music and sports. Learning a skill well and becoming one of the best in the world are different goals.
- No floor yet. If you can't answer anything, not even a guess, the source is too far ahead. Go one level down to Sunday's missing basics, then come back.
How do you run this week in Ritsu?
Every step above works with a notebook and a closed book. The hardest one to keep alone is Monday's: a question you have to answer before you see the explanation, and feedback that names what your answer missed. Checking yourself against the source catches a missing fact; an idea you have backwards can still look as if it matches the page.
Ritsu runs the same week on your own source. The paired episode names the five jobs as Ritsu's own framework, and says what that framework is not:
The clips below come from another of our videos, which runs a psychology chapter on memory through the same loop.
1. Drop in the source and set one goal. Upload a PDF or paste a web article, type the goal in your own words and start. The source comes back as an ordered path of units, each with its learning objectives and Points of Knowledge, and every item is traced to its page, which covers Sunday's map.
2. Check the floor. Type /prereq and a concept, and you get the knowledge it needs in learning order, from foundational to advanced, with a short note on each. If a basic is missing, it goes first.
3. Answer before you read. /askme puts one question to you at a time. On the memory chapter it asked the learner to explain, in their own words, the memory cycle and what happens at each of its three stages. Hint 1 and Hint 2 wait underneath, but the answer box comes first.
4. Read the verdict, then retry. Alone, you can only check your answer against the source; you can't easily see which idea you swapped. On a practice problem the learner swapped two ideas, and in that session the feedback named the error precisely: "You have reversed the definitions of automatic and effortful processing and missed the core concept of how skills transition between the two over time." That is the heat-island repair again, a missing connection named before any explanation, so the next attempt has a target. When an answer is wrong, Ritsu flags the misconception and offers a guided retry.
5. Add it to Review. Press Add to Review and the session comes back after one day, then three, seven, 16 and 35 days, so Saturday's return is scheduled for you.
Two more things help when you study alone. Every question and explanation shows the page of your file it came from, so the source stays the referee. And a web article works as well as a chapter: the remember what you read online use case shows the same loop on a pasted article.
Try it on the one source you need this week. Upload it, set one goal, and answer the first question before you read any explanation. You will see where you are actually starting before Ritsu explains anything. Start Learning Free — Free forever · No credit card · No time limit.
FAQ
Q: Am I too old to learn something new?
No, not for almost anything you would study. The 2023 learning-rate study's datasets ran from fifth grade through college to adult learners. The clearest age limit is narrow: the ability to learn a new language's grammar to a native-like level declines after the late teens.
Q: Does a growth mindset help you learn?
A little, and mostly for some students. Two meta-analyses by Victoria Sisk and colleagues (2018) found weak overall links between mindset and achievement, and weak average effects from mindset interventions, with some benefit for students with low socioeconomic status or at academic risk. Belief helps you start; attempts with feedback do the improving.
Q: Why am I struggling to learn?
Usually because the material assumes something you never learned, you read more than you answer, or nothing tells you what your answers missed. Test the basics first, two sentences each with the page closed. Then answer questions before you reread, and correct each answer against the source.
Q: How do I learn a new subject on my own?
Start with a map: find a good syllabus or textbook table of contents and the prerequisites it assumes. Learn one small unit at a time, test yourself at the end of each, keep a list of what you got wrong, and review it on a schedule. Without a teacher, regular self-testing is your feedback.
Q: Can Ritsu build this week's plan from my own source?
Yes. Upload a chapter, slides, a lecture or a web article, and Ritsu turns it into an ordered path of small units traced to its pages. /askme questions you before it explains, /prereq lists what a concept needs, and Add to Review brings each session back on a schedule.
The takeaway
Where you start was decided before tonight. How many honest attempts you make from here is decided this week.
Your week: tonight, turn one source into questions and test what it assumes. Monday to Wednesday, answer before you read, then correct. Thursday, try each idea on a new case. Saturday, answer everything from memory. Start tonight with the source you need this week and let Ritsu ask the first question. Start Learning Free
Sources
- Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. https://doi.org/10.1207/s15516709cog1302_1
- Hartshorne, J. K., Tenenbaum, J. B., & Pinker, S. (2018). A critical period for second language acquisition: Evidence from 2/3 million English speakers. Cognition, 177, 263–277. https://doi.org/10.1016/j.cognition.2018.04.007
- Koedinger, K. R., Carvalho, P. F., Liu, R., & McLaughlin, E. A. (2023). An astonishing regularity in student learning rate. Proceedings of the National Academy of Sciences, 120(13), e2221311120. https://doi.org/10.1073/pnas.2221311120
- Lee, H., Lichand, G., Barnard, C., Klotz, L., Thille, C., Kim, Y., & Domingue, B. W. (2026). Revisiting the regularity of student learning rate: Sensitivity to which observations are included [Preprint]. arXiv. https://arxiv.org/abs/2605.01690
- Macnamara, B. N., Hambrick, D. Z., & Oswald, F. L. (2014). Deliberate practice and performance in music, games, sports, education, and professions: A meta-analysis. Psychological Science, 25(8), 1608–1618. https://doi.org/10.1177/0956797614535810
- Recht, D. R., & Leslie, L. (1988). Effect of prior knowledge on good and poor readers' memory of text. Journal of Educational Psychology, 80(1), 16–20. https://doi.org/10.1037/0022-0663.80.1.16
- Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/10.1177/0956797617739704
- U.S. Environmental Protection Agency. What are heat islands? https://www.epa.gov/heatislands/what-are-heat-islands (read 2026-10-18)
By Ritsu Team · Last updated October 18, 2026
