Math AI is designed around real-world application, so the strongest Math AI explorations are grounded in genuine data — collected, scraped, or sourced from a credible dataset — rather than abstract theoretical modeling. If you’re a Math AI student, examiners want to see you interpreting numbers, not just manipulating symbols.
What Distinguishes a Strong Math AI Exploration
- Real, specific data (your own collected data is often stronger than a downloaded dataset, because it shows personal engagement)
- A clear statistical or modeling technique appropriate to that data
- Interpretation written in context — explaining what the numbers actually mean, not just stating them
- A thoughtful discussion of the limitations of your model or data collection method
15 Topic Ideas for Math AI (HL and SL)
Statistics & Data Analysis:
- Investigating whether there’s a correlation between screen time and sleep quality among students at your school (self-collected survey data)
- Analyzing whether home advantage is statistically significant in a sports league using real match data
- Comparing exam performance across different study techniques using a controlled survey and t-test analysis
- Investigating income inequality in a country using the Gini coefficient and Lorenz curve
Financial Mathematics: 5. Comparing the long-term value of different mortgage or loan repayment strategies using amortization models 6. Modeling the impact of compound interest frequency on long-term savings 7. Investigating whether a specific investment strategy (e.g., dollar-cost averaging) outperforms lump-sum investing using historical market data
Modeling & Optimization: 8. Using linear programming to optimize a real logistics problem (e.g., minimizing delivery costs for a small business) 9. Modeling traffic flow at a local intersection and testing an alternative light-timing scheme 10. Optimizing seating/space allocation in a venue using geometric and algebraic constraints
Probability: 11. Investigating the fairness of a lottery or game of chance using probability distributions and real outcome data 12. Modeling queue waiting times using probability distributions (Poisson or exponential models) with real data collected at a location
Networks & Graph Theory: 13. Using graph theory to find the most efficient route between locations (e.g., a delivery route or a public transport network) 14. Modeling a social network’s structure and analyzing connectivity patterns
Trigonometry & Geometry Applications: 15. Investigating the mathematics behind GPS positioning (triangulation) using real coordinate data
How to Collect Strong, Original Data
The highest-scoring Math AI explorations often involve primary data collection: running your own survey, timing your own experiment, or scraping publicly available data (sports statistics, government open data portals, financial market data) rather than relying on a textbook dataset that every other student in the world has access to.
Common Mistakes in Math AI Explorations
- Using a dataset that’s too small to justify meaningful statistical conclusions
- Applying a statistical test without checking whether its assumptions are actually met by the data
- Presenting results (a correlation coefficient, a regression equation) without interpreting what they mean in real-world terms
- Ignoring outliers or anomalies in the data instead of addressing them in the analysis
If you’re unsure how long your exploration should be, see our guide on word count and page limits by subject.
Final Tip
Before finalizing your topic, ask: “Can I realistically collect or find at least 20-30 data points for this?” Math AI explorations with too little data almost always struggle to justify a meaningful statistical conclusion, regardless of how interesting the topic is.