
The best IB Maths IA examples live in two places: the official IBO exemplar library and curated collections that tag work by grade, route and level, such as Clastify’s Maths IA archive. Your single best next move is not to hunt for a “perfect topic.” Pick two exemplars from your own route (Analysis & Approaches or Applications & Interpretation) and level, then copy their structure, their reflection style, and their word balance between narrative and appendix. Swap in your own data and interest afterwards. Tibertutor’s IA exemplars, built by working IB examiners, are worth reviewing alongside the official PDFs because they show the reasoning behind each mark, not just the finished script; for detailed course structures, see International Baccalaureate (IB) / IGCSE courses - Linda Mandarin.
Meeting all five IA criteria consistently matters more than chasing an unusual topic, and exemplar study, not topic novelty, drives most score improvements.
| Point | Details |
|---|---|
| Start with exemplars, not topics | Read two exemplars in your route and level before choosing your own question. |
| Scope tightly | Narrow projects with one variable and a clear method score higher than sprawling ones. |
| Reflect throughout | Weave critique of your own model into every section, not just the final paragraph. |
| Separate narrative from proof | Keep the main text under roughly 2,200 words; move long workings to appendices. |
| Use examiner-built tools | Tibertutor’s exemplars, mark-scheme explanations, mock exams and IA test-builder mirror each stage from idea to final check. |
Ready-to-adapt research questions save the panic of staring at a blank page. Each one below names the technique, the suggested route and level, and a realistic data source, drawing on the kind of topic pool RevisionTown’s 60-example collection has popularised among IB students.
Calculus and optimisation
Modelling and differential equations
Statistics and probability
Sequences and number theory
Geometry and trigonometry
Discrete maths and graph theory
Scope every idea to what you can finish in the time available, and log any data permissions (school surveys, third-party datasets) before you start collecting.
Every IA, AA or AI, SL or HL, is graded against five criteria confirmed in the official subject briefs: presentation, mathematical communication, personal engagement, reflection, and use of mathematics.
Exemplar IAs usually keep the main body to around 1,800 to 2,200 words, reserving long calculations, raw data tables and full derivations for appendices. The narrative explains what you did and why; the appendix proves it.
A simple planning timeline: pick a topic and check feasibility with a quick pilot dataset (week 1), collect and clean full data (weeks 2 to 3), build and test your model (weeks 4 to 5), write and revise with a peer or teacher check (weeks 6 to 7). Tibertutor’s guide to the IB Maths Internal Assessment walks through this timeline in more depth.

IA-ready checklist: title and research question stated in one sentence; rationale explaining personal interest; correct maths notation throughout; at least one reflective comment per section; appendices holding raw data and long working.
Pro Tip: Write your reflection sentences as you go, not at the end. Examiners can tell when reflection was bolted on the night before submission.
Examiner reports and annotated exemplars repeat the same handful of complaints. Over-ambitious scope is the most cited cause of lost marks in exploration work, according to patterns visible across curated exemplar sets like RevisionTown’s collection, where narrower, well-executed projects consistently outperform sprawling ones.
Any survey, interview or school dataset needs recorded consent, and any copied method or dataset needs a citation. Sloppy sourcing reads as a integrity issue even when it is only carelessness.
Picture an AI SL exploration asking whether a logistic model fits the spread of a rumour through a school year group better than a linear one. It opens with a one-sentence research question and a short, genuine rationale: the student noticed how fast news travelled through their own friend group.

The mathematics section defines the logistic differential equation, derives the general solution, and only then introduces the collected data (daily counts of students who had heard the rumour, gathered with teacher permission). It fits both a linear and logistic model, compares R² values, and explains in plain language why the logistic curve’s ceiling effect matches real behaviour better.
The reflection does not wait for a final paragraph. After the model comparison, the student flags that self-reported “when did you hear this” data is unreliable and estimates how much that could skew the curve’s inflection point. That single, quantified caveat is what separates a competent script from a strong one, since examiner-annotated exemplars consistently show personal engagement and clear reflection as the gap between a 6 and a 7. The appendix holds the raw survey data and full regression output. The main text stays under 2,000 words and never once repeats a calculation the reader can find in the appendix.
Original does not mean obscure. The strongest explorations take an ordinary situation, a bus route, a card game, a family recipe, and ask a precise mathematical question nobody else in your cohort would think to ask about it. AA subject brief guidance points toward projects built around a clear theoretical question with rigorous algebraic development, while the AI equivalent rewards projects that justify real modelling choices and interrogate their own limitations rather than simply plugging numbers into a formula.

Creativity often shows up in the framing, not the technique. Two students can both use linear regression, but the one who asks “does my school canteen’s queue length predict how late I am to my next lesson?” stands out more than one modelling generic exam scores, simply because the question forces a genuinely personal data-collection method.
It also shows in how a student handles a model that fails. A rumour-spread model that turns out not to fit well, discussed honestly, with a hypothesis about why, often scores better than a tidy model that happens to fit perfectly on the first attempt. Examiners read hundreds of scripts a season; the ones that stick are the ones where you can hear an actual person thinking, not a template being filled in.
Test feasibility with a five-minute pilot before committing weeks to a topic: can you actually get the data, and does a quick sketch of the maths look tractable? Talk to your IB coordinator early if you are unsure whether your route or level fits the question.
— Oliver
Tibertutor builds its IA resources with practising IB examiners, so every exemplar and mark-scheme explanation reflects how real scripts are actually graded, not a generic template.
Use them in sequence: exemplars while choosing your idea, mark-scheme notes while drafting, mock exams once your model is built, and a final test-builder check before submission.
| Point | Details |
|---|---|
| Stage 1: idea | Read two exemplars in your route to see what a well-scoped question looks like. |
| Stage 2: draft | Check your notation and structure against examiner mark-scheme explanations. |
| Stage 3: feedback | Sit a Maths AI or AA mock exam to test the underlying technique under time pressure. |
| Stage 4: final check | Run the IA test-builder on the specific skill your exploration relies on before submitting. |
Pro Tip: Read the mark-scheme explanation for “personal engagement” before you write your rationale paragraph, not after. It changes how you phrase your own opening sentence.
Tibertutor is the alternative to piecing together scattered blog posts and free PDFs for your Maths IA prep. Every exemplar, mark-scheme explanation and mock exam is written by working IB examiners and interlinked so a single weak spot, say, a shaky grasp of hypothesis testing, leads you straight from a mock exam question to the notes and practice tests that fix it. No other IB platform pairs examiner-built IA exemplars with this level of progress tracking and performance analytics across Maths, Biology, Chemistry and Physics.
If your IA depends on a modelling or statistics technique from the AI syllabus, run it through the Maths AI test-builder to confirm you can execute it under exam conditions before you write it up. Start your 7-day free trial today and see exactly which topics need work before your IA deadline arrives.
Read the Analysis and Approaches subject brief and Applications and Interpretation subject brief directly from ibo.org, then browse Tibertutor’s IA guide and what IA actually requires for next steps.
A good topic connects a genuine personal interest to a clear, narrow research question, such as modelling queue times at your school canteen rather than a broad, generic subject like “statistics in sport.”
Consistent strength across all five criteria matters more than one flash of brilliance; strong personal engagement and detailed, ongoing reflection are what most reliably separate top scripts from good ones.
Neither route is inherently easier; AA emphasises algebraic and theoretical rigour while AI emphasises real-data modelling and technology use, so the better fit depends on whether you prefer proof-style work or applied statistics.
Difficulty varies by student, but second-order differential equations and rigorous proof-based number theory, both found at AA HL, are widely considered among the most demanding areas for an IA.
Official exemplar student work and examiner commentary are published through ibo.org, and Tibertutor’s IA exemplars offer examiner-annotated alternatives that map directly to the same five assessment criteria.