Time Alloted
Standard Level (SL) – 1.5 hours
Higher Level (HL) – 2 hours
Math AI SL & HL
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What is IBDP Math AI? Why Study it?
The IBDP Mathematics: Applications and Interpretation course is designed for students interested in the practical application of mathematical concepts to real-world contexts. This course emphasizes the use of mathematics to model and solve problems in diverse fields such as statistics, financial mathematics, and networks. It is ideal for students who enjoy working with data and are interested in fields like social sciences, business, and economics. By studying this course, students develop strong analytical skills, technology proficiency, and the ability to interpret and communicate mathematical results effectively.
IB DP Math AI Syllabus: Topics + Overview
Designed for both Standard Level (SL) and Higher Level (HL) students, the course covers a range of topics that are crucial for understanding and applying mathematics in real-world scenarios. Higher-level study requires a minimum of 240 class hours, while standard-level study requires a minimum of 150 class hours. The syllabus includes key areas such as statistics, calculus, and mathematical modeling.
Topic
Included in both SL and HL
Only HL
Numbers and Algebra
Recommended Learning:
±33 Hours Total
Sub-topics
Scientific notation, arithmetic and geometric sequence and series and their applications, simple treatment of logarithms and exponentials, simple proof and approximations and errors.
Recommended Learning: 16 Hours
Additional Sub-topics
Laws of logarithms, complex numbers and practical applications, matrices and their applications for solving systems of equations for geometric transformations, and their applications to probability.
Recommended Learning: 29 Hours
Functions
Recommended Learning:
±40 Hours Total
Sub-topics
Recommended Learning: Creating, fitting and using models with linear, exponential, natural logarithm, cubic and simple trigonometric functions.
Recommended Learning: 31 Hours
Additional Sub-topics
Use of log graphs, graph transformations, creating, fitting and using models with further trigonometric logarithmic, rational, logistic and piecewise functions.
Recommended Learning: 42 Hours
Geometry and Trigonometry
Recommended Learning:
±40 Hours
Sub-topics
Volume and surface of 3d solids, right-angled and non-right-angled trigonometry including bearings, surface area and volume of composite 3d solids, establishing optimum positions and paths using Voronoi diagrams.
Recommended Learning: 18 Hours
Additional Sub-topics
Vector concepts and their applications in kinematics, applications of adjacency matrices, and tree and cycle algorithms.
Recommended Learning: 46 Hours
Statistics and Probability
Recommended Learning:
±33 Hours
Sub-topics
Collecting data and using sampling techniques, presenting data in graphical form, measure of central tendency and spread, correlation using Pearson’s product-moment and Spearman's rank correlation coefficients, regression, calculating probabilities, probability diagrams, normal distribution, Chi-squared test for independence and goodness of fit.
Recommended Learning: 36 Hours
Additional Sub-topics
Binomial and Possion distributions, designing data collection methods, tests for reliability and validity, hypothesis testing and confidence intervals.
Recommended Learning: 52 Hours
Calculus
Recommended Learning:
±33 Hours
Sub-topics
Differentiation including graphical behavior of functions and optimization, using simple integration and the trapezium/trapezoidal rule to calculate areas of irregular shapes.
Recommended Learning: 19 Hours
Additional Sub-topics
Kinematics and practical problems involving rates of change, volumes of revolution, setting up and solving models involving differential equations using numerical and analytic methods, slope fields, couple and second-order differential equations in context.
Recommended Learning: 41 Hours
Development of Investigational, Problem-Solving and Modeling Skills
Recommended Learning:
±33 Hours
Sub-topics
Recommended Learning: 30 Hours
Additional Sub-topics
Numbers and Algebra
Topic
Recommended Learning:
±33 Hours Total
Included in both SL and HL
Sub-topics
Scientific notation, arithmetic and geometric sequence and series and their applications, simple treatment of logarithms and exponentials, simple proof and approximations and errors.
Recommended Learning: 16 Hours
Only HL
Additional Sub-topics
Laws of logarithms, complex numbers and practical applications, matrices and their applications for solving systems of equations for geometric transformations, and their applications to probability.
Recommended Learning: 29 Hours
Functions
Topic
Recommended Learning:
±40 Hours Total
Included in both SL and HL
Sub-topics
Recommended Learning: Creating, fitting and using models with linear, exponential, natural logarithm, cubic and simple trigonometric functions.
Recommended Learning: 31 Hours
Only HL
Additional Sub-topics
Use of log graphs, graph transformations, creating, fitting and using models with further trigonometric logarithmic, rational, logistic and piecewise functions.
Recommended Learning: 42 Hours
Geometry and Trigonometry
Topic
Recommended Learning:
±40 Hours
Included in both SL and HL
Sub-topics
Volume and surface of 3d solids, right-angled and non-right-angled trigonometry including bearings, surface area and volume of composite 3d solids, establishing optimum positions and paths using Voronoi diagrams.
Recommended Learning: 18 Hours
Only HL
Additional Sub-topics
Vector concepts and their applications in kinematics, applications of adjacency matrices, and tree and cycle algorithms.
Recommended Learning: 46 Hours
Statistics and Probability
Topic
Recommended Learning:
±33 Hours
Included in both SL and HL
Sub-topics
Collecting data and using sampling techniques, presenting data in graphical form, measure of central tendency and spread, correlation using Pearson’s product-moment and Spearman's rank correlation coefficients, regression, calculating probabilities, probability diagrams, normal distribution, Chi-squared test for independence and goodness of fit.
Recommended Learning: 36 Hours
Only HL
Additional Sub-topics
Binomial and Possion distributions, designing data collection methods, tests for reliability and validity, hypothesis testing and confidence intervals.
Recommended Learning: 52 Hours
Calculus
Topic
Recommended Learning:
±33 Hours
Included in both SL and HL
Sub-topics
Differentiation including graphical behavior of functions and optimization, using simple integration and the trapezium/trapezoidal rule to calculate areas of irregular shapes.
Recommended Learning: 19 Hours
Only HL
Additional Sub-topics
Kinematics and practical problems involving rates of change, volumes of revolution, setting up and solving models involving differential equations using numerical and analytic methods, slope fields, couple and second-order differential equations in context.
Recommended Learning: 41 Hours
Development of Investigational, Problem-Solving and Modeling Skills
Topic
Recommended Learning:
±33 Hours
Included in both SL and HL
Sub-topics
Recommended Learning: 30 Hours
Only HL
Additional Sub-topics
Download full sub-topic list for the IB DP Math AI SL and HL
Download SyllabusIB DP Math AI Exams and Past Papers: Overview
Paper 1
For both SL and HL
40% of SL Final Exam Grade
30% of HL Final Exam Grade
Example Question
Statistical Analysis
Use statistical analysis to determine the correlation between two data sets provided.
Paper 2
For both SL and HL
40% of SL Final Exam Grade
30% of HL Final Exam Grade
Example Question
Mathematical Model
Create and interpret a mathematical model to predict future trends based on the provided data.
Paper 3
For HL only
NA
20% of HL Final Exam Grade
Example Question
Algorithms
Analyze the efficiency of different network algorithms in optimizing resource allocation.
Internal Assessment
For both SL and HL
20% of SL Final Exam Grade
20% of HL Final Exam Grade
Example Topic
Investigating the impact of different interest rates on long-term investment strategies.
Download all the free past papers
Download Free Test PaperIs IB Math AI Difficult?
The difficulty of the course depends on the student’s background in mathematics and their interest in applying mathematical concepts to real-world scenarios. IBDP Mathematics: Applications and Interpretation can be challenging, particularly at the Higher Level, as it requires a deep understanding of statistical analysis, mathematical modeling, and the use of technology in problem-solving. HL is recommended for students with strong analytical skills who plan to pursue careers in data science, economics, business, or other fields that require advanced mathematical modeling. SL is suitable for students who want to develop practical mathematical skills without delving too deeply into theoretical aspects. However, with consistent practice, support, and dedication, students can excel in this course and develop strong analytical skills that are highly valued in many academic and professional fields.
May 2023 Score Distribution
Ascend Now Examiner Tips
Practice Data Analysis Regularly
Priya
IB DP Tutor
Use Technology Wisely
Naman
IB DP Tutor
Plan and Manage Your Internal Assessment
Vidhi
IB DP Tutor
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