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IBDP · May 2027 Exam Prediction

IB Math AI HL & SL

Topic Prediction Guide

Paper 1, Paper 2 & Paper 3 (HL) subtopic frequency analysis for IBDP May 2027. Statistics, modelling and technology — every subtopic mapped.

4

Years Analysed

5

Topics Mapped

HL&SL

Both Levels

P1·P2·P3

All Papers

Predictions are based on historical past-paper frequency analysis (May 2021–2025) and are a study aid — not leaked or guaranteed exam content.

#	Topic	Subtopic	M21	M22	M23	M24	Freq	Avg Marks	HL only?	May 2027
IB MATH AI — PAPER 1 (SHORT RESPONSE, CALCULATOR) SUBTOPIC FREQUENCY \| May 2021–2024 \| SL & HL
All short-response questions \| Calculator required \| Statistics heavily weighted \| SL: HL-only rows greyed \| AI Paper 1 ≠ AA Paper 1 — very different structure
TOPIC 4: STATISTICS & PROBABILITY — dominant topic in AI (heaviest weighting)
4.1	Statistics	Descriptive statistics — mean, median, mode, standard deviation, IQR, box plots, cumulative frequency	✔	✔	✔	✔	4	8–14	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
4.2	Statistics	Correlation & regression — Pearson r, Spearman rank, scatter plots, linear regression y=ax+b, interpolation/extrapolation	✔	✔	✔	✔	4	8–12	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
4.3	Probability	Probability — basic rules, tree diagrams, Venn diagrams, conditional probability, independent events	✔	✔	✔	✔	4	6–10	SL&HL	⭐⭐⭐⭐⭐
4.4	Distributions	Normal distribution — P(a<X<b) with GDC, inverse normal, standardisation (z-scores)	✔	✔	✔	✔	4	8–12	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
4.5	Distributions	Binomial distribution — P(X=k), P(X≤k) using GDC, mean = np, variance, applications	✔	✔	✔	✔	4	6–10	SL&HL	⭐⭐⭐⭐⭐
4.6	Hypothesis	Hypothesis testing — chi-squared test (independence/goodness of fit), t-test, setting up H₀ and H₁, p-value interpretation	✔	✔	✔	✔	4	8–14	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
4.7	Stats (HL)	Spearman's rank correlation — calculate, interpret, hypothesis test for correlation HL	✔	✔	✔	✔	4	6–10	HL	⭐⭐⭐⭐⭐ HL
4.8	Stats (HL)	Markov chains — transition matrices, steady-state probabilities, long-term behaviour HL	✔	—	✔	✔	3	8–12	HL	⭐⭐⭐⭐ HL

#	Topic	Subtopic	M21	M22	M23	M24	Freq	Avg Marks	HL only?	May 2027
TOPIC 2: FUNCTIONS — modelling with functions, all calculator-based in AI
2.1	Functions	Modelling — linear, quadratic, exponential, logistic, sinusoidal — fitting and interpreting	✔	✔	✔	✔	4	10–16	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
2.2	Functions	Sinusoidal functions — A, B, C, D in y=A sin(Bx+C)+D; period, amplitude, applications	✔	✔	✔	—	3	6–10	SL&HL	⭐⭐⭐⭐
2.3	Functions (HL)	Piecewise models, logistic functions, log-log and semi-log linearisation, scaling data HL	✔	✔	—	✔	3	6–10	HL	⭐⭐⭐⭐ HL
TOPIC 1: NUMBER & ALGEBRA — financial maths, sequences, applications
1.1	Num & Alg	Financial mathematics — compound interest, depreciation, amortisation, annuities using GDC TVM	✔	✔	✔	✔	4	8–12	SL&HL	⭐⭐⭐⭐⭐ CERTAIN
1.2	Num & Alg	Sequences & series — arithmetic, geometric, sigma notation, applications and modelling	✔	✔	✔	✔	4	6–10	SL&HL	⭐⭐⭐⭐
1.3	Num & Alg (HL)	Complex numbers — Cartesian form, Argand diagram (HL); used in modelling contexts HL	—	✔	✔	—	2	5–8	HL	⭐⭐⭐ HL
TOPIC 3: GEOMETRY & TRIGONOMETRY — applied and 3D contexts
3.1	Geo & Trig	3D geometry — volumes and surface areas of solids, right triangles in 3D, bearings	✔	✔	✔	✔	4	6–10	SL&HL	⭐⭐⭐⭐
3.2	Trigonometry	Sine rule, cosine rule, area of triangle — applied problems, non-right triangles	✔	✔	✔	—	3	5–8	SL&HL	⭐⭐⭐⭐
3.3	Voronoi (HL)	Voronoi diagrams — nearest neighbour, constructing from perpendicular bisectors, applications HL	✔	✔	✔	✔	4	8–14	HL	⭐⭐⭐⭐⭐ HL
TOPIC 5: CALCULUS — basic calculus in applied/modelling contexts (AI is lighter on calculus than AA)
5.1	Calculus	Differentiation — polynomials, tangents/normals, max/min, increasing/decreasing in context	✔	✔	✔	✔	4	6–10	SL&HL	⭐⭐⭐⭐
5.2	Calculus	Integration — area under curve in context, definite integral with GDC, kinematics (distance from v(t))	✔	✔	✔	—	3	5–8	SL&HL	⭐⭐⭐⭐
5.3	Calculus (HL)	Differential equations — modelling with DEs, slope fields, Euler's method, separation of variables HL	✔	✔	✔	✔	4	10–16	HL	⭐⭐⭐⭐⭐ HL
5.4	Calculus (HL)	Further calculus — L'Hôpital's rule, improper integrals, limits in modelling contexts HL	✔	—	✔	—	2	6–10	HL	⭐⭐⭐ HL

#	Topic	Subtopic / Context	M21	M22	M23	M24	Freq	Marks	May 2027
IB MATH AI — PAPER 2 (EXTENDED RESPONSE, CALCULATOR) KEY TOPICS \| May 2021–2024 \| SL & HL
Extended multi-part questions \| Real-world context always present \| Interpretation = marks \| SL: HL-only topics greyed
STATISTICS — dominant in Paper 2 extended questions
4A	Stats Extended	Complete statistical study — sample data, regression, correlation, hypothesis test + interpretation in context	✔	✔	✔	✔	4	20–30	⭐⭐⭐⭐⭐ CERTAIN
4B	Probability	Probability extended — conditional probability, expected value, combined distributions in real context	✔	✔	✔	—	3	12–18	⭐⭐⭐⭐
FUNCTIONS — modelling extended questions
2A	Modelling	Extended modelling investigation — fit model to data, find parameters, predict, evaluate model appropriateness	✔	✔	✔	✔	4	20–28	⭐⭐⭐⭐⭐ CERTAIN
NUMBER & ALGEBRA
1A	Financial Maths	Loans, mortgages, investments — multi-step with TVM solver; find monthly payment, total interest, balance	✔	✔	✔	✔	4	12–18	⭐⭐⭐⭐⭐
HL ONLY — ADDITIONAL TOPICS IN PAPER 2
HL.1	Graph Theory (HL)	Minimum spanning tree (Prim/Kruskal), Chinese Postman, Travelling Salesman — weighted graphs HL	✔	✔	✔	✔	4	14–20	⭐⭐⭐⭐⭐ HL
HL.2	DEs & Modelling (HL)	Differential equations in extended real-world context — population growth, cooling, spread of disease HL	✔	✔	✔	✔	4	16–24	⭐⭐⭐⭐⭐ HL
HL.3	Advanced Stats (HL)	Bivariate analysis — non-linear regression, log linearisation, residuals; Spearman test extended HL	✔	✔	—	✔	3	12–18	⭐⭐⭐⭐ HL

Year	Investigation Theme	Primary Topics	Key Methods Used	HL-Only Content	May 2027 Signal
IB MATH AI HL — PAPER 3 THEME HISTORY \| May 2021–2024 \| HL ONLY
1 extended investigation per paper \| Real data provided \| Multiple methods applied progressively \| Calculator required \| Modelling + stats focus
M21	Environmental data analysis — temperature and species population modelling over time	Stats, Modelling, Calculus	Regression, hypothesis test, DE modelling, slope fields	DEs + Advanced Stats	Environmental/ecological data likely again
M22	Sports/physical data investigation — fitting models to performance data, predicting outcomes	Stats, Functions, Modelling	Multiple regression models, chi-squared, normal distribution, prediction intervals	Advanced Stats	Real-world data modelling very likely
M23	Financial/economic modelling — loan repayment, investment growth, inflation modelling	Finance, Functions, Stats	TVM, DE for continuous growth, regression on economic data, Spearman correlation	DEs + Spearman	Financial modelling could recur
M24	Medical/epidemiological data — disease spread, vaccination modelling, statistical analysis of health data	Stats, DEs, Modelling	SIR model using DEs + Euler, hypothesis tests on medical data, normal/binomial	DEs + Markov	Real-world health/population data likely

About IB Math AI

IB Mathematics: Applications and Interpretation (AI) is the applied mathematics pathway. It emphasises statistics, modelling and the use of technology. AI HL is rigorous and demanding in its own way — the gap between AI SL and HL is significant, with HL adding graph theory, differential equations, Markov chains, Voronoi diagrams and advanced statistics.

Also studying Analysis & Approaches? See the AA prediction page →

AI HL vs SL — key differences

›Paper 1: SL = 90 min / 80 marks / 40% | HL = 2 hrs / 110 marks / 30%
›Paper 2: Same extended format, more marks at HL, HL-only questions
›Paper 3: HL only — 60 min / 55 marks / 20%
›HL-only content: Graph theory, Voronoi diagrams, Markov chains, DEs, Spearman test, complex numbers, log-log linearisation
›Teaching hours: 150 hrs SL vs 240 hrs HL

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