Math AI SL & HL

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Of Ascend Now Students got 6+/7 in Math AI

83%

Improved their Math AI score by 1 point or more

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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.

Your track record in the Math AI SL & HL course is one of the key factors that will determine your college acceptances. Work with certified Math AI SL & HL Examiners to get that 7/7.
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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 Syllabus

IB 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

Time Alloted
Standard Level (SL) – 1.5 hours
Higher Level (HL) – 2 hours

Format
Calculator allowed with compulsory short-response questions based on the syllabus.

Content
Students solve problems using statistical techniques, financial mathematics, and mathematical models.

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

Time Alloted
Standard Level (SL) – 1.5 hours
Higher Level (HL) – 2 hours

Format
Calculator allowed, with compulsory extended-response questions based on the syllabus.

Content
Students apply mathematical concepts to solve complex real-world problems.

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

Time Alloted
NA

Format
Calculator allowed, consisting of extended-response problem-solving questions.

Content
In-depth problem-solving tasks using advanced statistical and mathematical modeling techniques.

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

Time Alloted
NA

Format
A mathematical exploration on a topic of the student’s choice, focusing on the application of mathematical techniques to real-world scenarios.

Content
12-20 pages in length.

Example Topic

Investigating the impact of different interest rates on long-term investment strategies.

Download all the free past papers

Download Free Test Paper

Is 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

Standard Level
Higher Level
40,272
Number of SL Students
22,609
Number of HL Students
4.5
SL Mean Score
4.9
HL Mean Score

Ascend Now Examiner Tips

Practice Data Analysis Regularly

"Engage with a variety of data sets regularly to strengthen your ability to interpret and analyze statistical results accurately."

Priya

IB DP Tutor

15+ years of experience teaching Maths

Use Technology Wisely

"Master the use of technology for solving complex problems, particularly in areas such as statistical analysis and financial mathematics."

Naman

IB DP Tutor

8+ years of experience teaching Maths

Plan and Manage Your Internal Assessment

"Choose a topic that genuinely interests you for your IA. Thorough planning and time management are crucial for producing a high-quality exploration."

Vidhi

IB DP Tutor

7+ years of experience teaching Maths

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