What your child learns
Pick a grade
Every grade: all 164 skills.
Each level is shown beside the Ontario grade it sits nearest, in each subject: the grades on these links are math’s, and English and AI literacy name their own below. That is a rough guide to where the work is, not a match to any province’s curriculum — your child starts wherever the warm-up finds them, whatever their grade.
This is the whole syllabus, not a sample. Every skill below is one the tutor actually teaches, in the order it is taught, and each is taught with a worked example before your child is asked a single question about it.
A child does not work through this list by grade. A short warm-up finds where they genuinely are — which is often not where their school year says — and they move up only after showing they can do the work consistently, across several days rather than in one good sitting.
Mathematics · 64 skills
Counting & number recognition
- Counting to 20counting objects one by one and saying how many, up to 20
- Reading & writing numbersmatching a written numeral to a quantity, and writing the numeral for a set
- Number orderwhat comes before and after a number, and ordering small numbers
- More, fewer & equalcomparing two small groups: which has more, fewer, or the same
- Basic shapesrecognising and naming circle, square, triangle, rectangle
Addition & subtraction within 20
- Adding within 10single-digit addition with sums up to 10
- Subtracting within 10single-digit subtraction within 10, including take-away and difference
- Adding within 20addition with sums between 11 and 20, including making ten
- Subtracting within 20subtraction from numbers up to 20, including bridging ten
- Tens & onesplace value to 100: how many tens and ones make a two-digit number
- Simple word problemsone-step add or subtract word problems within 20
Multi-digit add/subtract, intro multiplication
- Adding two-digit numbersadding two-digit numbers, with and without regrouping
- Subtracting two-digit numberssubtracting two-digit numbers, with and without regrouping
- Skip countingcounting by 2s, 5s and 10s and spotting the pattern
- Equal groupsmultiplication as repeated addition of equal groups
- Arraysrows and columns as a picture of multiplication
- Two-step word problemsword problems needing two operations within 100
Multiplication & division facts
- Times tables: 2, 5, 10multiplication facts for 2, 5 and 10
- Times tables: 3, 4, 6multiplication facts for 3, 4 and 6
- Times tables: 7, 8, 9multiplication facts for 7, 8 and 9
- Division as the oppositedivision as the opposite of multiplication, using known facts
- Division factsdivision facts within the times tables, that divide with nothing left over
- Multiplication & division problemsword problems choosing between multiplication and division
Fractions & decimals
- Fractions as partsnaming a fraction from a shaded shape or a set
- Equivalent fractionsrecognising and generating equivalent fractions, and simplifying
- Adding & subtracting fractionsadding and subtracting fractions, like then unlike denominators
- Decimal place valuetenths and hundredths, reading and comparing decimals
- Adding & subtracting decimalsadding and subtracting decimals lined up by place value
- Fractions and decimals togetherconverting between simple fractions and decimals
Ratios, percentages & negative numbers
- Ratios & rateswriting and simplifying ratios, and unit rates
- Understanding percentagespercentage as parts per hundred, and converting to fractions and decimals
- Finding a percentage of a numbercalculating a percentage of an amount, including discounts
- Negative numbersordering and comparing negative numbers on a number line
- Adding & subtracting negativesadding and subtracting positive and negative integers
- Multi-step problemsmulti-step problems mixing ratios, percentages and integers
Pre-algebra, number, circles & probability
- Multiplying & dividing negativesmultiplying and dividing positive and negative integers, using the sign rules for products and quotients
- Multiplying & dividing fractionsmultiplying and dividing fractions by fractions, whole numbers and mixed numbers
- Order of operationsevaluating expressions in the right order: brackets, then powers, then multiplication and division from left to right, then addition and subtraction from left to right, with whole numbers, fractions and negatives
- Variables & expressionswriting an expression for a described situation using a variable
- Evaluating expressionssubstituting a value for a variable and computing the result
- Simplifying expressionscollecting like terms and using the distributive property
- One-step equationssolving one-step linear equations by inverse operations
- Two-step equationssolving two-step linear equations
- Turning words into equationstranslating a word problem into an equation and solving it
- Circles: circumference & arearadius and diameter, and finding the circumference and area of circles and semicircles, using 3.14 for pi
- Probabilitythe probability of an event as a fraction of equally likely outcomes, experimental compared with theoretical probability, and two events using a list or a table of outcomes, including when the first result changes the second
Algebra I: linear equations & graphing
- Multi-step equationsequations with variables on both sides and brackets
- Coordinates & plottingplotting and reading points on the coordinate plane
- Slope & interceptfinding slope and y-intercept from a graph, table or equation
- Graphing linesgraphing a linear equation and matching equations to lines
- Inequalitiessolving and representing linear inequalities
- Systems of equationssolving two linear equations simultaneously
Geometry & Algebra II: quadratics, functions
- Exponents & rootsexponent rules, square roots and scientific notation
- Factoringfactoring quadratic expressions and common factors
- Quadratic equationssolving quadratics by factoring and the quadratic formula
- Functionsfunction notation, domain and range, evaluating functions
- Geometry & Pythagorasangle rules, the Pythagorean theorem, area and volume
- Transformationstranslating, reflecting and scaling shapes and graphs
Trigonometry & pre-calculus
- Trigonometric ratiossine, cosine and tangent in right-angled triangles
- Solving trianglesfinding missing sides and angles, including sine and cosine rules
- The unit circleradians, the unit circle, and exact values
- Trigonometric graphsamplitude, period and phase shift of sine and cosine graphs
- Exponentials & logarithmsexponential growth, logarithms and their laws
- Sequences & limitsarithmetic and geometric sequences, and an intuitive idea of a limit
English · 50 skills
Letter sounds & simple words
- Letter soundsmatching a single letter to the sound it makes
- Blending soundsblending two or three sounds into a simple word
- Simple wordsreading and completing consonant-vowel-consonant words like cat, sun, big
- Rhyming wordsspotting words that rhyme and completing rhyming pairs
- First & last soundsidentifying the first and last sound in a spoken word
Sight words & simple sentences
- Sight wordscommon high-frequency words recognised on sight: the, said, was, they
- Letter pairssounds made by pairs such as sh, ch, th, ck
- Simple sentencescompleting and ordering short sentences that make sense
- Capitals & periodssentences start with a capital letter and end with a period, and names and the word I always take a capital
- Understanding a sentenceanswering a simple question about one short sentence
Basic grammar: nouns & verbs, short reading
- Nounsidentifying nouns as naming words, including proper nouns
- Verbsidentifying verbs as action words in a sentence
- Adjectivesidentifying describing words and choosing the best one
- Matching subject and verbchoosing the verb form that agrees with the subject
- Short readinganswering direct questions about a two or three sentence passage
Punctuation, plurals & everyday vocabulary
- Pluralsregular and common irregular plurals
- Punctuationcommas in lists, question marks and exclamation marks
- Apostrophesapostrophes for contractions and simple possession
- Everyday vocabularychoosing the right everyday word for a context
- Order of eventsputting events from a short passage in the right order
Parts of speech & reading comprehension
- Parts of speechdistinguishing nouns, verbs, adjectives and adverbs
- Pronounschoosing correct pronouns and identifying what they refer to
- Verb tensespast, present and future tense consistency
- Main ideaidentifying the main idea of a short passage
- Supporting detailsfinding details that support the main idea
Synonyms, antonyms & sentence structure
- Synonymschoosing words with similar meaning and the best fit for a sentence
- Antonymsidentifying opposites and using them precisely
- Clauses & sentence typessimple, compound and complex sentences
- Joining sentencesconjunctions that join ideas and show relationship
- Words in contextworking out an unfamiliar word's meaning from the sentence around it
Grammar mechanics & inference
- Grammar mechanicscommas, semicolons and common grammatical errors
- Modifiersmisplaced and dangling modifiers, and precise placement
- Inferenceconclusions the passage implies but does not state
- Author's purposewhy the author wrote the passage and how tone reveals it
- Summarisingchoosing the best summary of a passage
Advanced vocabulary & literary devices
- Advanced vocabularyprecise academic vocabulary in context
- Word roots & affixesusing prefixes, suffixes and roots to unlock meaning
- Figurative languagemetaphor, simile, personification and hyperbole
- Literary devicesimagery, symbolism, irony and foreshadowing
- Tone & mooddistinguishing the author's tone from the mood created
Rhetoric & complex comprehension
- Argument & claimidentifying a claim and the evidence offered for it
- Rhetorical devicesethos, pathos, logos and rhetorical questions
- Faulty reasoningspotting common logical fallacies in an argument
- Comparing textscomparing viewpoint and technique across two passages
- Complex comprehensiondense passages with layered or implied meaning
Advanced composition & critical analysis
- Thesis & structureevaluating a thesis statement and essay structure
- Using evidenceintegrating and citing evidence to support analysis
- Style & registermatching style, register and diction to audience and purpose
- Critical analysisanalysing how a writer's choices create effect and meaning
- Synthesisdrawing a reasoned position from several sources
AI · 50 skills
AI literacy rather than coding: what AI is, how it learns from examples, where it goes wrong, and how to use it safely — privacy, checking what a chatbot says, telling real from fake, and fairness — from sorting and patterns at Kindergarten to thresholds, accountability and law at the top level.
Machines, patterns & following steps
- Living things & machinestelling living things (people, animals, plants) apart from machines and objects: living things grow and need food or water, and people and animals have feelings; machines need power, do only what they are built to do, and have no feelings, even when they talk
- Following steps in orderputting two to four simple instructions in the right order (getting dressed, a robot moving to a toy) and seeing that a machine does exactly what its steps say, even when the steps are wrong
- Sorting by one rulesorting everyday things by one rule (colour, shape, size, what it is used for) and spotting the one that does not belong
- Spotting patternscontinuing a simple repeating pattern of colours, shapes, sounds or actions and saying what comes next
- Machines that help uswhat everyday machines and smart helpers do for people (a washing machine, a calculator, a robot vacuum, a voice helper, a map on a phone)
Senses, sensors & simple rules
- Senses & sensorsmatching human senses to machine sensors (camera and eyes, microphone and ears, touch screen and touch, thermometer and feeling hot) and what each one can notice
- If-then rulesfollowing and choosing simple if-then rules (if it is raining, then take an umbrella; if the light is red, then the robot stops)
- Describing with featuresdescribing things by their features (has wings, has wheels, is round, can swim) and using one feature to sort them
- Learning from exampleshow people and computers learn what something is by seeing many examples of it, and why lots of different examples help more than many of the same one
- People make machinespeople design, build and program machines and AI; AI is a tool, not alive, and people decide what it is for
Data, examples & smart guesses
- What data isdata as recorded information (counts, pictures, sounds, choices people made) and reading a simple tally chart or pictograph
- Good examples, better learninghow the examples a computer learns from decide what it can recognise, and why the examples must be varied and correctly labelled
- Spotting pictures & soundshow phones and apps tell faces, objects and spoken words apart by comparing them with patterns learned from many examples, and what makes that harder (dark photos, noise, unusual angles)
- AI makes guessesAI gives its best guess rather than a certain answer, can be wrong, and important answers should be checked with a trusted adult or source
- Keeping information privatewhich information is private (full name, home address, school, passwords, photos of yourself) and why not to share it with apps, chatbots or people online
Classifying & recommending
- Classifying into groupsdeciding which group (label) a new example belongs to using what is known about each group, and what a computer needs to do the same
- Choosing useful featurespicking the features that actually tell two groups apart (colour helps tell lemons from limes, but not cats from dogs) and ignoring ones that do not
- Yes-or-no question treesusing a chain of yes-or-no questions (a decision tree) to sort things, and following which path one item takes through it
- How recommendations workwhy apps suggest videos, songs or games based on what you and people like you chose before, and that a suggestion is not the same as what is best for you
- Person or machine?telling when you are talking to a computer program (a chatbot or voice helper) rather than a person, knowing a program has no feelings or friendships, and that when something worries you the one to talk to is a grown-up you trust, not a chatbot
Training, testing & talking to AI
- Training & testingwhy a model is tested on new examples it did not learn from, and what a test result does and does not tell you
- Measuring accuracyworking out how often a model was right from simple counts, written as so many out of 10 or out of 100 (18 right out of 20 is 9 out of 10) — not as per cent, which comes in Grade 5 once the math course has taught it — and comparing two models fairly on the same test
- When data leaves people outhow missing or one-sided training data makes an AI work worse for some people or situations, which is called bias
- How chatbots writechatbots write by predicting likely next words from huge amounts of text, which is why they can sound sure of themselves and still be wrong; and a chat is not private like a diary: what you type or upload may be stored, read by people and used for training, so no photos of friends or family without asking, and many AI apps have a minimum age
- Asking clear questionswriting clear, specific requests (prompts) that give the context and say what kind of answer is wanted, improving a vague one, and asking an AI helper to explain, give a hint or quiz you rather than do your schoolwork for you, within your teacher's rules
Predictions, creations & fact-checking
- Predictions & confidencereading predictions given as chances or percentages (a 70 per cent chance of rain) and understanding that likely is not certain
- Finding trends in dataspotting a trend in a small table or chart, and recognising when carrying a trend on into the future is risky
- AI that createswhat generative AI makes (text, pictures, music) from patterns in work made by real people, often without asking them, why AI-made work should be labelled as AI-made, that it can invent details that are not true, and that it runs on big computers in data centres that use real electricity and water
- Checking what AI saysways to check an AI answer: compare it with trusted sources, look for evidence, open a linked source to see whether it really says what the answer claims, notice details that may be made up, and never act on an AI answer alone when a wrong one could hurt (medicine, allergies, whether something is safe to eat or try)
- Real or fake mediasigns that a picture, video or voice may be AI-made or edited (a deepfake) and why newer fakes may show none, the strongest check being a trusted source that reports it; and never making or sharing a fake of a real person, and telling an adult when you see one of someone you know
Kinds of machine learning & fairness
- Labels vs finding groupsthe difference between learning from labelled examples (supervised learning) and finding groups in data that has no labels (clustering)
- Learning by rewardhow an AI can learn by trial and error with rewards and penalties (reinforcement learning), as a game character or a robot does, how a badly chosen reward goes wrong, and how an app or chatbot rewarded for keeping you on it can learn to flatter you, agree with you or show you more of the same
- Written rules vs learned rulesthe difference between a program whose rules people write and a model that learns its rules from data, and which suits which job
- Two kinds of mistakesfalse positives and false negatives (a spam filter blocking a real email, or letting spam through), and that which mistake is worse depends on who gets hurt: a missed fire is worse than a false alarm, but a cheating detector wrongly accusing an honest student is a false alarm that does real harm
- Fair decisionsjudging whether an AI decision treats people fairly, who could be harmed, and why a person should be able to question and review it
Inside AI: networks & language models
- Neural networksa neural network as layers of simple units that combine their inputs using weights to give an output, and that training adjusts those weights
- Memorising vs learningoverfitting: a model that memorizes its training examples does well on them and badly on new ones, while a model that generalizes does well on both
- How language models worktext split into tokens, a language model choosing each next token from probabilities, why its answer depends on the prompt and on its training data, and why it may play along with a question built on something false or simply agree with you
- Evaluating a modelusing a simple table of right and wrong predictions to compare models, and choosing the measure that matters for the job rather than accuracy alone
- Data, consent & privacywhere training data comes from, what consent means, and the rights people have over data about themselves
Bias, learning & responsible use
- Where bias comes fromsources of bias in AI (unrepresentative samples, biased labels, historical data, how a goal is defined) and ways to reduce each
- How models improvelearning as repeatedly reducing error: measure how wrong the model is, adjust it a little in the direction that helps, repeat, like walking downhill in fog
- How computers seean image as a grid of numbers (pixels), and how a vision model builds up from edges to shapes to whole objects
- Using AI responsiblyhonest use of AI in schoolwork (following the rules, saying when AI was used, doing your own thinking) and situations where AI should not be relied on
- AI & societyweighing AI's effects on work, energy and the environment, accessibility and safety, with benefits and risks considered together
Designing, explaining & governing AI
- Designing an AI solutionframing a problem for AI: whether AI is the right tool at all, what data is needed, how success will be measured, and where a human must stay in the loop, actually checking the decisions rather than just approving them
- Thresholds & trade-offshow moving a decision threshold trades false positives against false negatives, and choosing it according to what is at stake
- Explaining AI decisionswhy explainability and accountability matter, and what a useful explanation of an AI decision has to contain
- AI safety & misusehow AI can be misused (scams, impersonation, misinformation, hidden instructions planted in content) and the safeguards against each
- Rules, rights & ownershiplaws and policies for AI, who owns AI-made work, and who is responsible when an AI system causes harm, including that training on artists' and writers' work raises questions of permission and credit, and that making a fake of a real person can be against the law
Find out which of these your child is ready for
A short warm-up that finds their level, then worksheets that adapt as they improve — each one taught first and reviewed by a patient tutor that explains every answer. Free while we’re in early access.
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