IB Maths AI IA: Criteria, Topics and Structure Guide

By Michael Thompson · Education Specialist; 10 years teaching the IB at Bromsgrove School · Published 4 September 2026 · Updated 4 October 2026

The IB Math AI IA - formally the Mathematical Exploration - is marked out of 20 and counts for 20% of your final grade at SL and HL, so understanding exactly how it is assessed matters before you write a single word. Every candidate up to and including the November 2028 session is marked against the same five criteria: Presentation, Mathematical communication, Personal engagement, Reflection and Use of mathematics. The IA asks you to investigate a question using the mathematical tools of Applications and Interpretation: statistics, modelling, probability, and technology-driven analysis. This guide covers the criteria, topic ideas, structure, and the specific things examiners reward - and penalise - so you can plan your exploration with confidence.

Key Takeaways

In This Article

  1. What the IB Math AI IA is and how it is weighted
  2. The five IB Math AI IA criteria explained
  3. IB Math AI IA topic ideas: SL and HL directions
  4. How technology use is assessed in an AI exploration
  5. What examiners want from the Reflection criterion
  6. IB Math AI IA structure, length, and common mistakes
  7. What to do next

1. What the IB Math AI IA is and how it is weighted

The IB Math AI IA (Internal Assessment) is a piece of independent mathematical work formally called the Mathematical Exploration. It sits at the heart of the DP Mathematics: Applications and Interpretation course, and it counts towards your final grade whether you are taking SL or HL. Unlike the written papers, the Exploration is submitted during the course rather than sat in an exam hall, which means your grade here is locked in before May examinations begin.

The exploration is marked out of 20 and counts for 20% of your final grade at both SL and HL. Every candidate up to and including the November 2028 session is assessed against the same five criteria, the model also used for Maths AA:

CriterionFocusMarks
A: PresentationA well-organised, coherent and concise exploration4
B: Mathematical communicationCorrect notation, symbols, terminology and representations4
C: Personal engagementEvidence of your own thinking, choices and interest3
D: ReflectionCritical reflection on results, methods and limitations3
E: Use of mathematicsRelevant, correct mathematics at the level of your course6
Total20

One counter-intuitive point worth noting early: SL and HL students are assessed against the same five criteria and the same mark totals. The only difference in the marking grid is Use of mathematics, which has separate SL and HL descriptors. A stronger mathematical treatment is therefore the main lever available to HL students aiming to separate themselves from SL candidates on this component.

Because the Applications and Interpretation course emphasises modelling and real-world data over abstract proof, the Exploration is a natural fit. A well-chosen modelling or statistics question gives you room for genuine personal engagement, meaningful reflection, and mathematics at the right level for your course.

First assessment May 2029: a new Mathematics: applications and interpretation course is first taught in August 2027 and first examined in May 2029 (IB). The IB's updates page and subject brief for that course list four exploration criteria, the same at SL and HL: Problem specification (4 marks), Abstraction (6), Computation (4) and Interpretation (6), still 20 marks in total. If you sit your exams in 2027 or 2028, it does not apply to you.

2. The five IB Math AI IA criteria explained

The IB Math AI IA is marked out of 20 across five criteria, the same five used for Maths AA. Each criterion is marked independently, so strong mathematics cannot make up for thin reflection, and a polished layout cannot rescue weak mathematics.

Criterion A: Presentation (4 marks)

Examiners want a well-organised, coherent and concise exploration with a clear aim. That starts with a focused, self-contained question - not a broad topic. "How does music streaming affect artist revenue?" is a topic. "Can a linear regression model predict monthly Spotify royalties from stream count for independent UK artists?" is a question. Marks are lost when the aim is vague, changes mid-exploration, or the structure makes the reader work out why each section is there.

Criterion B: Mathematical communication (4 marks)

Use correct mathematical notation, symbols and terminology throughout, define every variable and key term, and choose representations (formulae, tables, graphs, diagrams) that do analytical work. Calculator or spreadsheet notation such as 2^x or x*y in place of proper notation costs marks here.

Criterion C: Personal engagement (3 marks)

This rewards evidence that the exploration is genuinely yours: a question you chose for a reason, data you collected or selected yourself, and mathematical decisions that reflect your own thinking. A personal introduction ("I chose this because I love football") is not enough on its own if the mathematics could have been written by anyone.

Criterion D: Reflection (3 marks)

Results must connect back to the original question in context. Saying "the correlation coefficient is 0.87" is a calculation. Saying what that means for your specific dataset, noting the limitations of that figure, and assessing whether your model answers the original question - that is reflection. Section 5 of this article examines this criterion in more depth.

Criterion E: Use of mathematics (6 marks)

This is the highest-weighted criterion, and the one where mathematical depth matters most. Examiners look for mathematics that is relevant to your question, correct, and pitched at the level of your course, with a clear, justified choice of model or method. A common mistake is describing data collection at length without explaining why a particular model fits the situation. Accuracy counts, but so does understanding: showing only a GDC output with no explanation of what it means earns little credit. For HL students, a superficial model that an SL student could equally produce will not reach the top band, even if it is correctly executed.

3. IB Math AI IA topic ideas: SL and HL directions

The IB Math AI IA is designed around real-world data and applied modelling, which makes certain topic areas a natural fit. Statistics, regression and correlation, probability modelling, financial mathematics, and structured data analysis all align closely with the course's emphasis on contextual mathematics.

The counter-intuitive trap many students fall into is choosing a topic that is interesting but data-poor. A research question needs enough data points to run meaningful analysis, and enough mathematical structure to score well on Use of mathematics (worth 6 marks). Personal surveys of 20 classmates rarely do either.

Topic areas that suit the AI course

Example research questions (written for this guide)

SL-level examples:

HL-level examples:

The HL examples add a second mathematical layer (model comparison, residual critique, multi-variable consideration) and require explicit evaluation of model assumptions. That critical reflection is what separates a strong HL exploration from a competent SL one.

4. How technology use is assessed in an AI exploration

The Applications and Interpretation course is built around technology. GDCs, spreadsheets, and statistical software such as Desmos, GeoGebra, or R are expected tools, not optional extras. Examiners know this, which is why the "Use of Mathematics" criterion does not reward you for running a tool. It rewards you for showing that you understand what the tool did and why.

Generating output is the starting point, not the finishing line. The question the criterion is really asking is: can you reason about the mathematics behind the result, justify why you chose this method, and engage critically with what the output means?

The gotcha most students miss

A high R² from a regression does not automatically mean the model is good. A student who reports R² = 0.94 and concludes "the model fits well" has produced a calculator screenshot, not mathematical analysis. The R² value alone cannot tell you whether the relationship is actually linear, whether a single outlier is inflating the fit, or whether the model would hold outside the data range. Examiners flag this pattern repeatedly, and it costs marks across multiple criteria, not just one.

Weak vs stronger approach: regression example

(These examples are written for this guide to illustrate the contrast.)

DimensionWeak approachStronger approach
Method choiceRuns linear regression without explanationJustifies why linear regression suits the data structure; considers alternatives
Output reportedStates R² value and equationStates R², then plots and inspects residuals for patterns
CritiqueMoves on to the next sectionDiscusses what the model cannot account for, such as seasonality or confounding variables
Technology roleTool produces answerTool produces output; student interrogates it

The practical takeaway: after any software output, ask yourself one question before writing the next sentence. "What would a reader need to know to trust, or distrust, this result?" Answering that question in your IA is what "Use of Mathematics" at the higher performance bands actually looks like.

5. What examiners want from the Reflection criterion

Reflection is worth 3 marks. The distinction that separates the top band from the middle is not mathematical sophistication. It is whether you treat your results as the start of a conversation or the end of one.

Genuine critical engagement means asking what your maths cannot tell you, not just reporting what it can. That includes discussing the limitations of your model, explaining what the numbers mean in their real-world context, and considering what a different method or data set might reveal.

The contrast between weak and strong reflection is clearest with an example:

The second version does three things the first does not: it names a specific limitation, explains why that limitation matters, and proposes a concrete remedy.

One non-obvious pitfall: examiners can tell when reflection has been written after the mathematics was finished and appended as a tidy closing paragraph. Reflection woven into each analytical stage scores higher than a single retrospective block, because it demonstrates that your thinking shaped the exploration rather than tidied it up. Write a limiting comment each time you obtain a result, not once at the end.

6. IB Math AI IA structure, length, and common mistakes

A well-structured ib math ai ia follows a clear sequence that mirrors how a mathematician actually works through a problem:

  1. Introduction and research question - state what you are investigating and why it is mathematically interesting.
  2. Background context - explain any real-world setting, define variables, and establish the mathematical tools you will use.
  3. Mathematical exploration body - this is the bulk of the work: calculations, models, graphs, and GDC or software outputs with your own reasoning woven around them.
  4. Results and interpretation - draw meaning from your outputs, address limitations, and link findings back to the original question.
  5. Conclusion - reflect on what the mathematics revealed and what you would do differently.

On length: most successful explorations run between 12 and 20 pages. The IB does not set a formal word count, but examiners note that very short submissions often lack sufficient mathematical development, while padded ones tend to bury the reasoning under redundant graphs and re-stated outputs.

A counter-intuitive gotcha: an exploration with 15 pages of GeoGebra screenshots and no accompanying algebraic reasoning will score poorly on every criterion, not just Use of mathematics. The screenshots are evidence, not argument.

Most common mistakes to avoid:

7. What to do next

Your most urgent task has nothing to do with choosing a topic. Ask your teacher this week for your school's internal IA submission deadline. Schools typically set this several months before the IBO's external moderation window, and missing it usually means no extension.

Once you have that date, check which assessment model applies to you. If you sit your exams in 2027 or 2028, your exploration is marked against the five criteria on this page; the new course with different criteria is not examined until May 2029. Be wary of revision sites that already present the 2029 criteria as current.

Confirm both the deadline and your criteria with your teacher before you write a single line of your exploration.

FAQ

How many marks is the IB Math AI IA out of?

The IA is marked out of 20 across five criteria - Presentation (4), Mathematical communication (4), Personal engagement (3), Reflection (3) and Use of mathematics (6) - and these criteria apply to every candidate up to and including the November 2028 session.

What is the difference between IB Math AI SL and HL IA expectations?

SL and HL share the same five criteria and the same 20 marks, but Use of mathematics has separate SL and HL descriptors: HL students must show more sophisticated and rigorous mathematics to reach the top marks.

How long should an IB Math AI IA be?

There is no strict word count, but most successful explorations run to approximately 12-20 pages; below that range it is difficult to demonstrate sufficient depth across all five criteria.

Can I use software like Excel or GeoGebra in my IB Math AI IA?

Yes - technology use is expected in an AI exploration, but examiners want you to justify your method choices and interpret the outputs critically, not simply reproduce calculator or software results.

Are the IB Math AI IA criteria changing?

Not for anyone sitting exams in 2027 or 2028. A new Applications and Interpretation course is first examined in May 2029, and the IB's subject brief for it lists four criteria, the same at SL and HL: Problem specification (4 marks), Abstraction (6), Computation (4) and Interpretation (6), for 20 marks in total.

What are the IB Math AI IA grade boundaries?

Grade boundaries are set by the IBO after each exam session and vary year to year; the official boundaries for each session are published on the IBO results pages and through your school's IB coordinator.

What topics work well for an IB Math AI IA?

Topics that involve real data sets and allow for statistical modelling, regression analysis, or mathematical modelling of real-world phenomena tend to suit the AI course well - the key is choosing a question specific enough to require genuine mathematical work.

References