{"id":46026,"date":"2026-09-10T15:24:07","date_gmt":"2026-09-10T08:24:07","guid":{"rendered":"https:\/\/times.edu.vn\/?p=46026"},"modified":"2026-09-10T15:25:14","modified_gmt":"2026-09-10T08:25:14","slug":"ib-mathematics-ai-ia-topics","status":"publish","type":"post","link":"https:\/\/times.edu.vn\/en\/ib\/ib-mathematics-ai-ia-topics\/","title":{"rendered":"IB Mathematics AI IA topics 2026: How to choose the right exploration for your grade"},"content":{"rendered":"<p>Strong IB Mathematics AI IA topics should connect a meaningful real-world question with enough data, mathematical depth, and scope for critical reflection. Suitable ideas can come from statistics, regression, financial modelling, environmental data, functions, geometry, trigonometry, or, at HL, more advanced tools such as differential equations and Markov chains. The best topics are specific, researchable, and allow students to use more than one mathematical technique rather than simply describing a dataset.<\/p>\n<p>This guide explores effective IB Mathematics AI IA topic ideas for SL and HL, explains how to develop a focused research question, and highlights common topics that often lack sufficient depth.<\/p>\n<h2>What makes a strong IB Mathematics AI IA topic?<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/times.edu.vn\/wp-content\/uploads\/2026\/09\/IB-Mathematics-AI-IA-topics.webp\" alt=\"IB Mathematics AI IA topics\" width=\"1000\" height=\"667\" \/><\/p>\n<p>The <a href=\"https:\/\/times.edu.vn\/en\/ib\/ib-maths-ai\/\">IB AI<\/a> <sup><a href=\"#tooltip-ref-1\" class=\"tooltip-link\" data-tooltip=\"https:\/\/ibo.org\/university-admission\/latest-curriculum-updates\/dp-mathematics-applications-and-interpretation-updates\/\">[1]<\/a><\/sup> course is built around one core philosophy: Mathematics as a tool for understanding the real world. Your IA must reflect that philosophy at every stage, from data collection through to interpretation.<\/p>\n<p>A strong IA topic satisfies three conditions simultaneously. First, it must generate enough real-world data to allow genuine mathematical analysis, not fabricated or artificially limited datasets. Second, the mathematics must sit at or slightly above your syllabus level, meaning SL students should reach beyond basic descriptive statistics into inferential testing or non-linear modelling. Third, the topic must allow you to reflect critically on what your results actually mean in context, which is where many students lose marks they should have earned.<\/p>\n<p>One critical detail often overlooked is the distinction between doing the math and interpreting the math. IB examiners are trained to reward students who explain why a model breaks down at extremes, or what a correlation coefficient actually tells us about a relationship in the real world. That interpretive layer is what separates a Grade 5 paper from a Grade 7.<\/p>\n<p><strong>Key qualities of a high-scoring IB Mathematics AI IA topic:<\/strong><\/p>\n<ul>\n<li>Rooted in observable, measurable phenomena with accessible data<\/li>\n<li>Produces at least 30 data points for statistical validity<\/li>\n<li>Allows the use of multiple mathematical tools rather than one isolated technique<\/li>\n<li>Connects clearly to a personally meaningful context, which strengthens your Personal Engagement criterion<\/li>\n<li>Generates genuine uncertainty and room for critical reflection<\/li>\n<\/ul>\n\n\t<button class='btn-dang-ky' onclick=\"openPopup('popup1')\">\n\t\t<span class='text-effect-1'>\n\t\t\t<svg\n\t\t\t\txmlns='http:\/\/www.w3.org\/2000\/svg'\n\t\t\t\tviewBox='0 0 64 64'\n\t\t\t\twidth='24'\n\t\t\t\theight='24'\n\t\t\t\taria-label='Calendar icon'\n\t\t\t>\n\t\t\t\t<rect width='64' height='64' rx='6' fill='#caa15a'\/>\n\t\t\t\t<rect x='10' y='14' width='44' height='40' rx='4'\n\t\t\t\t\tfill='none' stroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='10' y1='24' x2='54' y2='24'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='22' y1='6' x2='22' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t\t<line x1='42' y1='6' x2='42' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t<\/svg>\n\t\t\tBook a Trial Class\n\t\t<\/span>\n\t<\/button>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-mathematics-ai-exam-technique\/\">IB Mathematics AI exam technique<\/a> 2026: The complete guide to scoring higher<\/p>\n<h2>IB Mathematics AI IA topic ideas using statistics and real-world data<\/h2>\n<p>Statistics and data analysis form the largest portion of the AI syllabus, and for good reason. This strand offers the widest range of accessible real-world datasets and the clearest pathway to meeting the IA&#8217;s complexity requirements.<\/p>\n<p>Drawing on years of experience at <a href=\"https:\/\/times.edu.vn\/\">Times Edu<\/a>, the statistics-based IAs that score highest tend to combine an inferential test (such as a t-test or chi-squared test) with a regression analysis, rather than treating each technique as a standalone exercise. The combination shows mathematical range and supports richer interpretation.<\/p>\n<p><strong>Sports and human performance<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<th colspan=\"1\" rowspan=\"1\">Topic idea<\/th>\n<th colspan=\"1\" rowspan=\"1\">Core mathematical tools<\/th>\n<th colspan=\"1\" rowspan=\"1\">Data source<\/th>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Home advantage in football leagues<\/td>\n<td colspan=\"1\" rowspan=\"1\">Two-sample t-test, chi-squared test<\/td>\n<td colspan=\"1\" rowspan=\"1\">Official league databases (e.g., fbref.com)<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Fatigue curve of marathon runners<\/td>\n<td colspan=\"1\" rowspan=\"1\">Exponential regression, logarithmic modelling<\/td>\n<td colspan=\"1\" rowspan=\"1\">Published race split data<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Body composition vs. Athletic output<\/td>\n<td colspan=\"1\" rowspan=\"1\">Pearson&#8217;s r, non-linear regression<\/td>\n<td colspan=\"1\" rowspan=\"1\">Sports science journals or public athlete databases<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>These topics work because sports data is both abundant and standardised. A student analysing home advantage across three seasons of a major football league will have no shortage of data points, and the research question writes itself cleanly.<\/p>\n<p><strong>Economics, business, and finance<\/strong><\/p>\n<p>One of the most effective IA topic ideas in this strand involves modelling a revenue function using local business data, then applying differential calculus to locate the profit-maximising price point. This works particularly well for HL students who can bring calculus tools into the analysis explicitly.<\/p>\n<p>Exchange rate volatility analysis is another strong option. By comparing currency pair data before and after a documented economic event, such as an election result or a major policy announcement, students can apply normal distributions and z-scores to quantify investment risk. The real-world anchor is clear, the data is freely available from financial databases, and the mathematical tools map directly onto the HL syllabus.<\/p>\n<p><strong>Social sciences and environmental data<\/strong><\/p>\n<p>For students with interests in psychology, sociology, or environmental science, cross-country datasets offer rich material. Gathering secondary data from sources like the World Bank or Our World in Data, and then using Pearson&#8217;s correlation coefficient to explore relationships between variables such as literacy rates and happiness indices, produces IAs that feel genuinely exploratory.<\/p>\n<p>Urban Heat Island Mapping is a particularly creative option. Students can plot temperature data at increasing distances from a city centre, test for linear or non-linear correlation, and then connect findings back to urban planning policy. The Voronoi diagram component adds a geometric layer that distinguishes the paper from purely statistical submissions.<\/p>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-mathematics-ai-common-mistakes\/\">IB Mathematics AI common mistakes<\/a> 2026: The errors that cost students the most marks<\/p>\n<h2>IB Mathematics AI IA topic ideas from modelling and functions<\/h2>\n<p>Modelling-based IAs ask students to fit mathematical functions to real-world phenomena. The AI course covers exponential, logistic, sinusoidal, and polynomial models, and a strong IA will justify why one model fits better than another, rather than simply reporting the result.<\/p>\n<blockquote><p>A common mistake we see is students who apply a linear regression to data that is visibly non-linear, then report an R-squared value without questioning what it means. Examiners penalise this. Your model choice must be justified, and your reflection must acknowledge where the model fails.<\/p><\/blockquote>\n<p><strong>Recommended modelling topics:<\/strong><\/p>\n<ul>\n<li>Bacterial or population growth modelled with a logistic function, including identification of the carrying capacity and inflection point<\/li>\n<li>Depreciation of a vehicle over time compared across different compound interest loan structures<\/li>\n<li>Sinusoidal modelling of seasonal temperature variation or tidal patterns with an analysis of amplitude and period<\/li>\n<\/ul>\n<p>For HL students, the Predator-Prey Simulation using coupled differential equations and Euler&#8217;s method is one of the most sophisticated and rewarding options available. It draws directly from HL-exclusive content, demonstrates genuine mathematical complexity, and produces results that are visually compelling through phase plane diagrams. The limitation here is that it requires careful scaffolding. Students who attempt this without a clear understanding of the underlying calculus often produce technically incomplete papers.<\/p>\n<p><strong>The Amortization and Investment Comparison<\/strong> is a business-focused modelling topic that performs consistently well at both SL and HL. By modelling the total cost of a major purchase, such as a car or property, under different financing structures including varying compound interest rates and depreciation schedules, students can produce a genuinely useful financial analysis. The personal relevance is easy to establish, which directly benefits the Personal Engagement score.<\/p>\n\n\t<button class='btn-dang-ky' onclick=\"openPopup('popup1')\">\n\t\t<span class='text-effect-1'>\n\t\t\t<svg\n\t\t\t\txmlns='http:\/\/www.w3.org\/2000\/svg'\n\t\t\t\tviewBox='0 0 64 64'\n\t\t\t\twidth='24'\n\t\t\t\theight='24'\n\t\t\t\taria-label='Calendar icon'\n\t\t\t>\n\t\t\t\t<rect width='64' height='64' rx='6' fill='#caa15a'\/>\n\t\t\t\t<rect x='10' y='14' width='44' height='40' rx='4'\n\t\t\t\t\tfill='none' stroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='10' y1='24' x2='54' y2='24'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='22' y1='6' x2='22' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t\t<line x1='42' y1='6' x2='42' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t<\/svg>\n\t\t\tBook a Trial Class\n\t\t<\/span>\n\t<\/button>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-mathematics-ai-command-terms\/\">IB Mathematics AI command terms<\/a> 2026: What they mean and how to respond correctly<\/p>\n<h2>IB Mathematics AI IA topic ideas from geometry and trigonometry<\/h2>\n<p>Geometry and trigonometry are less commonly used in AI IAs than statistics, but they offer a valid pathway for students whose strengths and interests align with spatial reasoning or design.<\/p>\n<p>Voronoi diagrams, which are part of the AI syllabus at both SL and HL, open up a range of interesting applications. Students have used them to analyse the optimal placement of emergency services, the territorial divisions of competing retail chains, or the structural geometry of natural formations such as honeycombs.<\/p>\n<p>Trigonometric modelling works well when students can connect it to cyclical real-world phenomena. Tidal height data over a lunar cycle, temperature variation across seasons, or even the swing arc of a pendulum can all be modelled using sinusoidal functions. The key is to collect enough data points and to go beyond simply fitting the curve. Students should test the model&#8217;s predictive accuracy against additional data they did not use in the original regression.<\/p>\n<blockquote><p>One critical detail often overlooked in geometry-based IAs is the need for a strong mathematical question. Geometric explorations can slide into descriptive territory if the student is not careful. Every geometric IA should still contain a clear, testable question, a method for answering it using mathematical tools, and a reflective conclusion that interrogates the limitations of the approach.<\/p><\/blockquote>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-books\/\">IB books<\/a> 2026: The complete guide to study materials for the IB Diploma<\/p>\n<h2>IB Mathematics AI IA topics to avoid and why they score poorly<\/h2>\n<p>Not all topic ideas are created equal. Some have been submitted so many times across the global IB community that examiners have seen them hundreds of times and know exactly where students go wrong.<\/p>\n<p><strong>Overused IB Mathematics AI IA topics:<\/strong><\/p>\n<table>\n<tbody>\n<tr>\n<th colspan=\"1\" rowspan=\"1\">Topic<\/th>\n<th colspan=\"1\" rowspan=\"1\">Why it scores poorly<\/th>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Golden ratio in nature or architecture<\/td>\n<td colspan=\"1\" rowspan=\"1\">Almost always descriptive rather than analytical; rarely reaches required complexity<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Simple linear regression on two obvious variables<\/td>\n<td colspan=\"1\" rowspan=\"1\">Produces a single R-value with no inferential depth; insufficient complexity<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Modelling a dropped ball using projectile motion<\/td>\n<td colspan=\"1\" rowspan=\"1\">More appropriate for Physics; rarely connects to AI syllabus tools specifically<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">Basic compound interest comparison<\/td>\n<td colspan=\"1\" rowspan=\"1\">Too computational; lacks genuine investigation or uncertainty<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">COVID-19 case number modelling<\/td>\n<td colspan=\"1\" rowspan=\"1\">Extremely overused; data quality issues; limited scope for original insight<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>A common mistake we see is students choosing these topics because they seem safe or manageable. In reality, they are harder to score well on because the examiner&#8217;s expectations are calibrated to hundreds of previous attempts, and any weakness is immediately visible.<\/p>\n<blockquote><p>The deeper issue with overused IA topics in IB AI is that they tend to limit the student&#8217;s ability to demonstrate genuine mathematical curiosity. When a topic has been done a thousand times, there is little room for original analysis or authentic personal engagement.<\/p><\/blockquote>\n\n\t<button class='btn-dang-ky' onclick=\"openPopup('popup1')\">\n\t\t<span class='text-effect-1'>\n\t\t\t<svg\n\t\t\t\txmlns='http:\/\/www.w3.org\/2000\/svg'\n\t\t\t\tviewBox='0 0 64 64'\n\t\t\t\twidth='24'\n\t\t\t\theight='24'\n\t\t\t\taria-label='Calendar icon'\n\t\t\t>\n\t\t\t\t<rect width='64' height='64' rx='6' fill='#caa15a'\/>\n\t\t\t\t<rect x='10' y='14' width='44' height='40' rx='4'\n\t\t\t\t\tfill='none' stroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='10' y1='24' x2='54' y2='24'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4'\/>\n\t\t\t\t<line x1='22' y1='6' x2='22' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t\t<line x1='42' y1='6' x2='42' y2='18'\n\t\t\t\t\tstroke='#ffffff' stroke-width='4' stroke-linecap='round'\/>\n\t\t\t<\/svg>\n\t\t\tBook a Trial Class\n\t\t<\/span>\n\t<\/button>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-maths-ai\/\">IB Maths AI<\/a>: Syllabus, Exam Structure &amp; Roadmap to an 7 in 2026<\/p>\n<h2>How to develop a focused research question from a broad IB Mathematics AI IA idea<\/h2>\n<p>The research question is where most students make their earliest and most consequential mistake. A broad interest area such as &#8220;football&#8221; or &#8220;climate change&#8221; is not a research question. It is a starting point.<\/p>\n<p>In our experience working with international students, the best research questions emerge from a structured narrowing process that forces specificity at each stage.<\/p>\n<p><strong>Step-by-step process for developing your IB AI IA research question:<\/strong><\/p>\n<ol start=\"1\">\n<li>Start with a domain you genuinely care about (sports, finance, environment, music, psychology)<\/li>\n<li>Identify a specific measurable variable within that domain that can change or vary<\/li>\n<li>Ask what mathematical relationship might exist between that variable and another observable quantity<\/li>\n<li>Determine what data you would need to test that relationship, and confirm it is accessible<\/li>\n<li>Frame the question so that it has a clear, mathematical answer, not just a descriptive one<\/li>\n<li>Check that the mathematics required to answer it matches or slightly exceeds your syllabus level<\/li>\n<\/ol>\n<p>For example, a student interested in basketball would move from &#8220;I want to study basketball&#8221; to &#8220;Does a team&#8217;s average possession percentage have a statistically significant effect on points scored per game across one NBA season?&#8221; That version is specific, measurable, and requires inferential statistical tools to answer properly.<\/p>\n<blockquote><p>The dataset dimension matters enormously. Using publicly available datasets from sources like Kaggle, the World Bank, Gapminder, or official sports databases gives your IA credibility and ensures your sample size is large enough for valid statistical conclusions. Aim for a minimum of 30 data points, and ideally more than 50 if you are running inferential tests.<\/p><\/blockquote>\n<p><strong style=\"color: #f00;\">&gt;&gt;&gt; Read more:<\/strong> <a class=\"xem-them-link\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-math-ai-hl-statistics-modelling\/\">IB Math AI HL Statistics Modelling<\/a>: 5-Step Framework for Paper 3 Score 7<\/p>\n<h2>Frequently asked questions<\/h2>\n<p><strong>What makes a good IB Mathematics AI IA topic?<\/strong><\/p>\n<p>A good IB Mathematics AI IA topic connects a real-world phenomenon to multiple mathematical tools from the AI syllabus, generates sufficient data for valid analysis, and allows for genuine critical reflection on results. The topic should feel personally meaningful to the student, since this directly supports the Personal Engagement criterion.<\/p>\n<p><strong>What are some strong IB Mathematics AI IA topic ideas for SL students?<\/strong><\/p>\n<p>Strong IA topic ideas for IB AI SL include home advantage analysis using chi-squared tests, logistic population growth modelling, exchange rate volatility before and after economic events, and correlation analysis between social indicators such as income and life expectancy. Each of these can be executed at SL level with appropriate data and methods.<\/p>\n<p><strong>What are some strong IB Mathematics AI IA topic ideas for HL students?<\/strong><\/p>\n<p>HL students should aim for topics that draw on HL-exclusive content, including coupled differential equations for predator-prey modelling, Markov chain transition matrices for market share prediction, ANOVA tests for multi-variable agricultural or biological experiments, and calculus-based optimisation of revenue or cost functions.<\/p>\n<p><strong>Can you use publicly available datasets as the basis of an IB Mathematics AI IA?<\/strong><\/p>\n<p>Yes, and in most cases this is the recommended approach. Sources such as Kaggle, the World Bank Open Data portal, Gapminder, government statistical agencies, and official sports databases all provide reliable, citable data. The key requirement is that you must process and analyse the data yourself, rather than simply reporting conclusions drawn by others.<\/p>\n<p><strong>What topics are overused in IB Mathematics AI IAs and should be avoided?<\/strong><\/p>\n<p>Overused IA topics in IB AI include the golden ratio in nature, simple two-variable linear regression, COVID-19 case number modelling, and basic compound interest comparisons. These topics score poorly not because the mathematics is wrong but because they rarely produce sufficient complexity or original insight to reach the higher mark bands.<\/p>\n<p><strong>How do you narrow a broad interest into a specific IB Mathematics AI IA research question?<\/strong><\/p>\n<p>Begin by identifying one measurable variable within your area of interest, then ask what other variable it might be related to. Frame the relationship as a testable mathematical question, confirm that enough data exists to answer it, and verify that the mathematical tools required align with your syllabus level. Specificity is the goal at every step.<\/p>\n<p><strong>What level of mathematical complexity is expected in an IB Mathematics AI IA?<\/strong><\/p>\n<p>The IA complexity expected in IB Mathematics AI is that your mathematics should reach or slightly exceed your syllabus level. For SL students, this means going beyond basic descriptive statistics into inferential tests or non-linear regression. For HL students, it means engaging with HL-exclusive tools such as differential equations, Markov chains, or multivariate statistical methods. Using only one mathematical technique, regardless of how cleanly it is executed, is rarely sufficient for a top mark.<\/p>\n<p><strong>Conclusion<\/strong><\/p>\n<p>At Times Edu, our 1-on-1 IB tutoring programme includes dedicated IA strategy sessions where our curriculum specialists help students identify the right topic for their level, interests, and university profile. If you are unsure whether your current topic idea has the depth to score in the top mark band, that is exactly the kind of question we help students answer before it is too late to change course. Reach out to our team to book a personalised academic consultation and get your IA development started on the right foundation.<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-right kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;right&quot;,&quot;id&quot;:&quot;46026&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0110\u00e1nh gi\u00e1 b\u00e0i vi\u1ebft&quot;,&quot;legend&quot;:&quot;0\\\/5 - (0 votes)&quot;,&quot;size&quot;:&quot;24&quot;,&quot;title&quot;:&quot;IB Mathematics AI IA topics 2026: How to choose the right exploration for your grade&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} - ({count} {votes})&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-right: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 24px; height: 24px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 19.2px;\">\n            <span class=\"kksr-muted\">\u0110\u00e1nh gi\u00e1 b\u00e0i vi\u1ebft<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p>Strong IB Mathematics AI IA topics should connect a meaningful real-world question with enough data, mathematical depth, and scope for critical reflection. Suitable ideas can come from statistics, regression, financial modelling, environmental data, functions, geometry, trigonometry, or, at HL, more advanced tools such as differential equations and Markov chains. The best topics are specific, researchable, &#8230; <a title=\"IB Mathematics AI IA topics 2026: How to choose the right exploration for your grade\" class=\"read-more\" href=\"https:\/\/times.edu.vn\/en\/ib\/ib-mathematics-ai-ia-topics\/\" aria-label=\"Read more about IB Mathematics AI IA topics 2026: How to choose the right exploration for your grade\">Read more<\/a><\/p>\n","protected":false},"author":15,"featured_media":45943,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","rank_math_title":"","rank_math_description":"","footnotes":""},"categories":[170],"tags":[],"class_list":["post-46026","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ib"],"_links":{"self":[{"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/posts\/46026","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/comments?post=46026"}],"version-history":[{"count":3,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/posts\/46026\/revisions"}],"predecessor-version":[{"id":46080,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/posts\/46026\/revisions\/46080"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/media\/45943"}],"wp:attachment":[{"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/media?parent=46026"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/categories?post=46026"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/times.edu.vn\/en\/wp-json\/wp\/v2\/tags?post=46026"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}