Which courses does AscentIQ cover?

IB Mathematics: Analysis and Approaches SL, Applications and Interpretation SL, and Applications and Interpretation HL — exercises generated for each course's syllabus and marked against the IB mark scheme. Analysis and Approaches HL isn't available yet; it's on our roadmap. We add courses one at a time rather than several superficially.

How does the marking actually work?

Your work is transcribed, then evaluated against the exercise's mark scheme one mark at a time. Each Method, Answer, and Reasoning mark is awarded or withheld with a written reason. Follow-through is applied, so an early error doesn't automatically cost you the marks that depend on it.

Can the AI get the maths wrong?

The maths isn't generated by AI. Every number in every exercise is computed deterministically before the AI is involved — the AI only writes the wording around values it's been given, and is blocked from producing numbers of its own.

Do I have to write by hand?

You don't have to, but the whiteboard is where the marking is worth most. Writing out formula, substitution, and answer lets each method mark be awarded or withheld on its own. Typing takes final answers only — quicker, but there's no working to mark, and nothing to review afterwards.

What do I need to use it?

A tablet with a stylus is the best experience — the whiteboard is built for it. A laptop or desktop works fully too. We wouldn't recommend a phone: the whiteboard needs more room than a phone screen gives it.

Seven days of full access — every chapter of your course, unlimited generated exercises, and per-mark feedback on the work you write out. No card required to start.

What happens when the trial ends?

If you'd like to continue afterwards, leave your details at ascentiq.io/continue or write to renzo@ascentiq.io, and you'll hear back by email — including about the asynchronous tutoring we're building on top of your results.

What is the asynchronous tutoring?

It's a tutoring method built on your results in the exercise mode — where your marks are lost, which topics are slipping, what to work on next — followed up at your own pace, without having to coordinate a live session. It's still in development, so there's no fixed format yet. If you'd like to hear how it will work, write to renzo@ascentiq.io.

← IB Math AI HL

IB Math AI HL · Topic 2

Functions

At SL this topic is about choosing a model. At HL it is also about building on one: composite and inverse functions, transformations, richer models — logistic, sinusoidal, piecewise — and the logarithmic scaling that turns a curved data set into a straight line you can read.

What the syllabus covers

Ten assessed sections: SL 2.1 to 2.6, shared with AI SL, and AHL 2.7 to 2.10, which are HL only.

SL 2.1Straight linesSlope–intercept, general and point–slope forms; gradient; parallel lines m₁ = m₂; perpendicular lines m₁ × m₂ = −1.

SL 2.2Functions, domain and rangeNotation such as f(x), v(t), C(n); the function as a mathematical model; the inverse as the undo operation, reflected in y = x.

SL 2.3Graphing skillsSketching from information given or a context; transferring graphs from the GDC screen to paper; graphing sums and differences with technology.

SL 2.4Key features of graphsMaxima and minima, intercepts, horizontal and vertical asymptotes; intersections of two curves found with technology.

SL 2.5The model familiesLinear mx + c; quadratic with axis, vertex and zeros; exponential growth/decay k·aˣ + c with its horizontal asymptote; direct/inverse variation; cubic; sinusoidal a·sin(bx) + d.

SL 2.6The modelling processChoose a model in context, fit its parameters, state a reasonable domain, justify the choice from data shape and curve properties, then read predictions back out.

AHL 2.7Composite and inverse functionsComposition in either order, the identity function, and finding f⁻¹(x).

AHL 2.8Transformations of graphsTranslations, reflections, stretches and the composition of several transformations.

AHL 2.9Further modelling functionsExponential and logarithmic models, sinusoidal models, logistic growth and piecewise models.

AHL 2.10Scaling and linearising data with logarithmsLogarithmic scales, and straightening a power or exponential relationship to find its parameters.

How it is examined

Modelling questions are about choosing, fitting and then answering in the context's own terms.

The GDC fits the parameters and finds intersections and extremes. The marks are for the choice of model, the values you took from the screen, and the sentence that returns the answer to the context.
Logistic and other bounded models have an asymptote that means something: the carrying capacity. Questions ask for it, for when growth is fastest, and for what the model says in the long run.
Sinusoidal models use radians at HL. Amplitude, period and the principal axis come from f(x) = a sin(b(x − c)) + d, with the period 2π/b.
Scaling data (AHL 2.10) is a reading skill. If ln y against ln x is a straight line the relationship is y = a xⁿ; if ln y against x is straight it is y = a bˣ. The marks go to turning gradient and intercept back into the parameters.
Composite and inverse functions are examined with exact values and with domains: the order of composition, the inverse found by swapping x and y, and the domain a composite is defined on.
Transformations are described one at a time and completely: the type, the direction and the size.

A marked question, mark by mark

A linearisation question where the gradient is read correctly and the intercept is not turned back into a parameter.

Practise modelling end to end, on fresh exercises

From SL 2.1 to AHL 2.10, every generated exercise comes back with each mark awarded or withheld and a reason — including the interpretation sentence that closes a modelling question.

Start your trial

New to the codes? M1, A1, R1 explained →

Linearising data · AHL 2.10

Seed 4M18

Two variables are modelled by y = a xⁿ. A graph of ln y against ln x is a straight line with gradient 3 and vertical intercept 1.5.

(a)Find the value of n.
(b)Find the value of a, correct to three significant figures.

Your work

ln y = n ln x + ln a

n = 3

a = 1.5

Marks awarded

M1Mark awarded. Took logarithms to reach ln y = n ln x + ln a, a straight line in ln x.
A1Mark awarded. n = 3, the gradient.
A1Mark not awarded. The intercept is ln a, not a. Here ln a = 1.5, so a = e^1.5 = 4.48.

Where marks are actually lost

The graphs are rarely the difficulty. Reading them back into parameters and context is.

Taking the intercept of a log–log or semi-log line as the parameter itself.

CostThe intercept is the logarithm of the parameter. Without undoing the logarithm the accuracy mark is lost.

Leaving the GDC in degree mode for a sinusoidal model.

CostThe period and every value from the model come out wrong together, and the accuracy marks go.

Composing functions in the wrong order — (f ∘ g)(x) written as g(f(x)).

CostEverything downstream is built on the wrong function. Follow-through can rescue later marks, but not the composition itself.

Stating a model's prediction as a bare number, without units or a sentence in context.

CostIn a modelling question the interpretation is marked. A number alone does not answer what was asked.