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Define the object, dataset, time period, variable or mathematical structure as precisely as possible.
IB Mathematics · Complete topic list
900 topic ideas – page 36 of 36.
Topics 876 to 900 with explanations, methods, course and equipment guidance.
Mathematics and applications
Compare 900 ideas clearly.
The list mixes calculus, statistics, modelling, geometry, number theory, computer science, sport, environmental topics and other areas. Each entry includes a short explanation and visible methods such as differential calculus, integral calculus, statistics or regression.
Planning guidance, not official topic approvalThe IA, EE, AA, Math AI, SL and HL classifications are editorial guidance. The current subject guide, assessment session, mathematical depth, focus and school approval remain decisive.
1
Select an idea. Titles and areas are starting points, not finished research questions.
2
Check A and C. These codes give an initial indication of assessment type, course and level.
3
Read P, M and S. They show possible independent direction, tools, and safety or data-protection needs.
| No. | Topic idea | A | C | P | M | S |
|---|---|---|---|---|---|---|
| 876 | Further artificial-intelligence experimentsData-augmentation method and robustness Compare augmentation methods using the same model. |
3 | 9 | 181112 | 1320 | 0 |
| 877 | Further artificial-intelligence experimentsFeature scaling and training convergence Train the same algorithm using different feature scaling methods. |
3 | 9 | 13781112 | 1213 | 0 |
| 878 | Further artificial-intelligence experimentsDecision threshold and error cost Vary the decision threshold of a fixed classifier. |
3 | 9 | 1481112 | 1320 | 0 |
| 879 | Further artificial-intelligence experimentsEnsemble size and prediction variance Combine increasing numbers of independently trained small models. |
3 | 6 | 18101112 | 121320 | 0 |
| 880 | Further artificial-intelligence experimentsHuman, AI, and human–AI collaboration Solve the same verified tasks under three conditions. |
3 | 2 | 18101112 | 132021 | 0 |
| 881 | Algorithms, digital measurement, and data qualityInput size and sorting runtime Compare algorithms on identical hardware with repeated trials. |
3 | 9 | 1371112 | 61320 | 0 |
| 882 | Algorithms, digital measurement, and data qualityObstacle density and A\* versus Dijkstra efficiency Generate grid maps with controlled obstacle density. |
3 | 9 | 13781112 | 161113 | 0 |
| 883 | Algorithms, digital measurement, and data qualityInterpolation method and spatial prediction error Remove selected points from a measured spatial field. |
3 | 6 | 1367810 | 31013 | 0 |
| 884 | Algorithms, digital measurement, and data qualityStep size and numerical-integration error Integrate experimental rate data using different methods. |
3 | 9 | 171112 | 31213 | 0 |
| 885 | Algorithms, digital measurement, and data qualitySampling rate and frequency estimation Record the same periodic signal at different sampling rates. |
3 | 2 | 171112 | 613 | 0 |
| 886 | Algorithms, digital measurement, and data qualityRepeated calculations and rounding error Compare mathematically equivalent calculations at limited precision. |
3 | 6 | 13671112 | 13 | 0 |
| 887 | Algorithms, digital measurement, and data qualityOcclusion and QR-code recognition Cover defined portions of QR codes at different correction levels. |
3 | 6 | 171112 | 13 | 0 |
| 888 | Algorithms, digital measurement, and data qualityImage compression and information loss Save identical images at different compression levels. |
3 | 9 | 1371112 | 713 | 0 |
| 889 | Algorithms, digital measurement, and data qualityPseudorandom generator and statistical uniformity Compare several generators or seed values. |
3 | 6 | 13781112 | 11317 | 0 |
| 890 | Algorithms, digital measurement, and data qualityBit-error pattern and detection probability Simulate different bit-error patterns. |
3 | 6 | 171112 | 113 | 0 |
| 891 | Geometry, probability, queues, and networksOptimal cylinder proportions Build equal-volume cylinders with different radius-to-height ratios. |
3 | 9 | 12381112 | 11320 | 0 |
| 892 | Geometry, probability, queues, and networksCorner cut and maximum box volume Cut different corner squares from equal sheets. |
3 | 9 | 1381112 | 113 | 0 |
| 893 | Geometry, probability, queues, and networksCircle packing and area efficiency Compare regular, staggered, and algorithmically generated arrangements. |
3 | 9 | 13781112 | 1313 | 0 |
| 894 | Geometry, probability, queues, and networksFolding pattern and paper compression strength Compare different folding patterns using equal paper area. |
3 | 6 | 181112 | 121320 | 0 |
| 895 | Geometry, probability, queues, and networksGeometric irregularity and die fairness Introduce controlled geometric asymmetry into model dice. |
3 | 6 | 181112 | 131320 | 0 |
| 896 | Geometry, probability, queues, and networksNumber of shuffles and card disorder Compare shuffle methods using numbered cards. |
3 | 6 | 181112 | 113 | 0 |
| 897 | Geometry, probability, queues, and networksDimension and random-walk return time Study random walks in different dimensions. |
3 | 6 | 181112 | 1613 | 0 |
| 898 | Geometry, probability, queues, and networksSingle versus multiple queues Simulate service times using cards, dice, or software. |
3 | 6 | 181112 | 1361320 | 0 |
| 899 | Geometry, probability, queues, and networksTraffic-light cycle and average delay Simulate an intersection using measured or synthetic arrival rates. |
3 | 9 | 1367811 | 11013 | 1 |
| 900 | Geometry, probability, queues, and networksNode failure and network robustness Remove random or highly connected nodes from a model network. |
3 | 6 | 181112 | 1131720 | 0 |
No topic ideas match this combination.
Code legend
What A, C, P, M and S mean.
The table stays narrow on a phone by replacing long descriptions with numeric codes. Entries may contain several P and M codes.
AAssessment type
- 1
- Internal Assessment (IA)
- 2
- Mathematics Extended Essay (EE)
- 3
- Potentially suitable for an IA or EE, depending on focus and mathematical depth
CCourse and level
- 1
- Mathematics AA SL
- 2
- Mathematics AA HL
- 3
- Mathematics AA at SL or HL
- 4
- Mathematics AI SL
- 5
- Mathematics AI HL
- 6
- Mathematics AI at SL or HL
- 7
- Mathematics AA or AI at SL
- 8
- Mathematics AA or AI at HL
- 9
- Mathematics AA or AI at SL or HL
PWays to demonstrate independent direction and personal engagement
- 1
- Own data, measurements, observations or experiment
- 2
- Own photographs, drawings, constructions or models
- 3
- Own sport, video, GPS or tracker context
- 4
- Own school or class survey or observation
- 5
- Own everyday, household, consumer or financial data
- 6
- Local context: environment, buildings, traffic, climate or nature
- 7
- Own programming, simulation or algorithm
- 8
- Personal interest: music, art, games, design or another hobby
- 9
- Public data selected, prepared and analysed independently
- 10
- Own conjecture, proof idea, generalisation or theoretical comparison
- 11
- Own modelling decision, construction, optimisation or adaptation
- 12
- Critical comparison of assumptions, errors, limitations or ethical issues
MMeasuring instruments, tools or data access
- 0
- No specialist physical instrument; a calculator, CAS, spreadsheet or open data may be sufficient
- 1
- Ruler, tape measure, calliper or protractor
- 2
- Balance or precision scale
- 3
- Contact thermometer or temperature data logger
- 4
- Infrared thermometer or thermal camera
- 5
- Multimeter or another electrical measuring instrument
- 6
- Stopwatch or timer
- 7
- Camera or smartphone for photographic and video analysis
- 8
- GPS device or fitness tracker
- 9
- Microphone, sound-level meter or audio-analysis software
- 10
- Light meter, light sensor or solar sensor
- 11
- Conductivity, pH or salinity meter
- 12
- Weather instruments, such as an anemometer or rain gauge
- 13
- Computer, spreadsheet, CAS, GeoGebra, Desmos or Python
- 14
- Survey form, data sheet or observation record
- 15
- Force sensor or spring balance
- 16
- Laboratory glassware, measuring cylinder or pipette
- 17
- Telescope, binoculars or a suitable camera
- 18
- Humidity or material-moisture sensor
- 19
- Non-invasive physiology sensor, such as heart-rate or reaction-time measurement
- 20
- Physical model, 3D printer or material samples
- 21
- Specialist school laboratory equipment
SSafety and data protection
- 0
- Likely to be low risk within normal school practice
- 1
- Supervision recommended, for example for heat, electricity, sport or traffic observation
- 2
- Carry out only in a school laboratory or with qualified supervision
- 3
- Sensitive personal or health data: consent, anonymisation and preferably secondary data; no medical self-intervention
From heading to investigation
A topic becomes workable only through independent decisions.
The table is designed to speed up the first step. The actual research question emerges through focus, mathematical choice and critical checking.
Select the mathematics
Decide which models, proofs, statistical procedures or optimisation steps can genuinely answer the question.
Plan independent direction
Use your own data, comparisons, modelling choices, extensions or proof ideas rather than reproducing a standard procedure.
Reflect on limitations
Examine assumptions, sources of error, data quality, model limitations, safety and possible improvements.
Personal engagement and independent direction
A personal connection is more than one sentence in the introduction.
The P codes indicate possible ways to shape an investigation independently. Independent thinking becomes visible through justified decisions, appropriate data selection, personal model variants, meaningful comparisons and critical reflection. A code does not guarantee a particular mark.
Review the IA requirementsFrequently asked questions
Use the topic list correctly.
Are the titles finished research questions?
No. They name a possible direction. A question for assessed work must be focused more narrowly, matched to the course and level, and connected to a clear mathematical method.
What does A = 3 mean?
The broad direction could be developed as an IA or Mathematics EE, depending on focus and depth. An EE will normally require a substantially deeper mathematical argument and an appropriate research scope.
Is C an official IB classification?
No. C is editorial guidance for Mathematics AA or Math AI and SL or HL. Final suitability depends on the specific research question and the current requirements.
Do I need to own the listed instruments?
No. M indicates typical or possible tools. Many topics can use open data, a spreadsheet, CAS, GeoGebra, Desmos or Python. Adapt the topic to resources that are genuinely available.
How should topics involving health or personal data be handled?
Prefer anonymised or publicly available secondary data. Original data collection needs consent, data protection, school approval and a low-risk method. Diagnosis, medication changes and invasive self-experimentation do not belong in a Mathematics project.
The complete list is also machine-readable.
The same 900 entries are available as plain text and bilingual JSON for search, accessibility and AI systems.
Authoritative foundations
Check the current curriculum version before starting.
The catalogue complements the PreLearning explanations. Current official IB documents and the school's instructions remain authoritative.