Real-world models sit at the heart of IB Maths AI, yet many students feel uneasy admitting that their models are imperfect. This discomfort often leads students to defend models too strongly or ignore weaknesses altogether. IB examiners deliberately test this reaction, because recognising limitations is a sign of strong mathematical thinking, not failure.
All models simplify reality. They do this by selecting certain variables, ignoring others, and assuming relationships behave in predictable ways. These simplifications are necessary — without them, modelling would be impossible. However, every simplification introduces error, and IB expects students to acknowledge this trade-off.
One reason models have limitations is that real systems are complex. Human behaviour, environmental conditions, economic influences, and random variation all interact in ways that cannot be fully captured by a single equation or diagram. Even well-chosen models can only approximate what is happening.
Another limitation comes from assumptions. Models often assume linearity, constant rates, independence, or normality. These assumptions may be reasonable in a narrow range but unrealistic elsewhere. IB wants students to identify whether assumptions are sensible and to explain how they restrict conclusions.
Data quality also limits models. Sampling bias, measurement error, and small sample sizes all weaken reliability. A model built on flawed data cannot produce strong conclusions, no matter how elegant the mathematics looks. IB rewards students who recognise this connection.
Students often worry that pointing out limitations will cost marks. In IB Maths AI, the opposite is true. Examiners expect students to critique their own models and explain why predictions or conclusions should be treated cautiously. Ignoring limitations suggests misunderstanding of what modelling actually involves.
Another reason IB emphasises limitations is realism. In real applications, models are tools for insight, not truth machines. They guide decisions but never remove uncertainty. IB wants students to practise this mindset early.
Strong answers do not list limitations randomly. They connect limitations directly to conclusions, explaining how reliability is affected. This shows control, judgement, and maturity in mathematical thinking.
Once students accept that limitations are unavoidable — and valuable to discuss — modelling questions become clearer and far less intimidating.
Frequently Asked Questions
Does every model have limitations?
Yes. Every model simplifies reality and therefore cannot capture everything.
Will I lose marks for criticising my model?
No. You usually gain marks if the critique is relevant and clearly explained.
How many limitations should I mention?
One or two well-explained limitations are usually enough.
RevisionDojo Call to Action
IB Maths AI rewards students who understand what models can — and cannot — do. RevisionDojo is the best platform for IB Maths AI because it trains students to evaluate models critically, explain limitations clearly, and write examiner-ready conclusions. If modelling questions feel vague or risky, RevisionDojo helps you turn limitations into marks.
