New IB Computer Science Syllabus: A Complete Guide

This is your complete breakdown of the brand new IB Computer Science curriculum, with first exams in 2027. The changes are significant, shifting the focus from memorization to genuine problem-solving, algorithmic thinking, and modern topics like Machine Learning.

At Lanterna, our expert tutors (who are all top IB graduates) have distilled everything you need to know into one place. We’ll cut through the jargon and give you the insider strategies to excel.

By using this guide, you will be able to:

• Understand the Core Changes: Instantly grasp the new two-theme structure and the removal of the old "Options."
• Master the New Assessments: Get a clear breakdown of the new Paper 1, Paper 2, and the updated Internal Assessment (IA) criteria.
• Strategize for the Case Study: Learn exactly what to expect from the new "Generative AI" case study and how to prepare for it.
• Plan Your 2-Year Journey: Use our recommended timeline to stay ahead from day one.
• Unlock a 7: Learn the specific strategies that separate top-scoring students from the rest.

1. The Big Picture: A New Philosophy

The IB has completely overhauled the Computer Science course. The old system, with its optional modules (like Databases or Web Science), is gone. Now, every student studies a single, unified core curriculum.

This new syllabus is built on two interconnected pillars:

• Theme A: Concepts of Computer Science: The theory, hardware, and ethics behind how computers work.
• Theme B: Computational Thinking and Problem-Solving: The practical application of programming and algorithms to solve real-world problems.

This change means the course is less about memorizing syntax and more about becoming a genuine computational thinker.

2. Decoding the Syllabus: The Two Themes

Theme A: Concepts of Computer Science

This is the theoretical foundation of your course. It covers four main areas:

• A1: Computer Fundamentals: You’ll explore the core of a computer: the fetch-execute cycle, the CPU, memory, and logic gates. HL Students go deeper, evaluating operating system concepts like paged vs. segmented memory, multitasking, and how compilers and interpreters translate code.
• A2: Networks: This covers everything from LANs and VPNs to cloud computing and cybersecurity. You’ll need to understand modern threats like DDoS attacks and the protocols that keep us safe.
• A3: Databases: Previously an option, this is now mandatory. You’ll learn how relational databases work and use SQL to manage data. HL Students will also study modern distributed and NoSQL databases.
• A4: Machine Learning (ML): This is the most exciting addition. You’ll be introduced to supervised, unsupervised, deep, and reinforcement learning. The course places a massive emphasis on the ethical implications of AI, forcing you to analyze issues like algorithmic bias and transparency. HL Students will dive into the technical architecture of neural networks and genetic algorithms.

Theme B: Computational Thinking & Problem-Solving

This is where you put the theory into practice. It includes:

• B1: Approaches to Computational Thinking: Learn to think like a programmer by breaking down complex problems using decomposition, pattern recognition, and abstraction.
• B2: Programming: Your school must now choose to teach the entire course in either Python or Java. You will master the fundamentals of your chosen language.
• B3: Object-Oriented Programming (OOP): Another former option that is now core content. You’ll learn to model real-world problems using classes. HL Students will master advanced OOP concepts like inheritance, polymorphism, and common design patterns across multiple classes.
• B4: Abstract Data Types (HL Only): This HL-exclusive unit covers the implementation of complex data structures like stacks, queues, linked lists, and binary trees, along with recursive algorithms.

3. The Big Decision: Java vs. Python

Your school will choose one language for the entire department. This choice affects your classroom learning, your IA, and which version of Paper 2 you will sit.

• Java: A highly structured language that strongly enforces OOP principles. It’s more verbose (requires more code for simple tasks) but provides excellent preparation for rigorous university-level computer science.
• Python: Known for its simplicity and readability. It’s faster to code and debug, making it great for students new to programming. It supports OOP, but doesn’t force it.

4. The Exams: A Complete Breakdown

The external assessments have been streamlined. The old Paper 3 for HL is gone.

Paper 1 Deep Dive

This paper tests your knowledge of Theme A. Section A has short-answer questions on computer fundamentals, networks, and ML. Section B is dedicated entirely to the Pre-seen Case Study, which is now mandatory for both SL and HL students. To score well, you must go beyond definitions. Examiners want you to evaluate and apply concepts to real-world scenarios.

Paper 2 Deep Dive

This paper tests Theme B and your practical problem-solving skills. The IB creates two separate versions: one for Java and one for Python. The questions focus on designing algorithms and writing code.

5. The Case Study: Generative AI

The pre-seen case study is now a major part of Paper 1 for everyone. The topic for the first assessment cycle is "Generative AI for Image Creation," based on a fictional company called Visionary Studios.

About a year before the exam, the IB will release a short booklet outlining the scenario. This booklet only provides context; it does not give you answers. Your job is to use it as a launchpad for your own deep research into the technology, its applications, and its ethical challenges (like copyright and algorithmic bias). You will get a clean copy of the booklet in the exam, so all your research must be memorized.

6. The Internal Assessment (IA): Your Computational Solution

The IA is a 35-hour software development project worth a huge portion of your final grade (30% at SL, 20% at HL). The biggest change is that you are no longer required to have a formal, external "client." While solving a real user's problem is still a great idea, you can now choose any problem of personal interest, as long as it is complex enough.

You must submit three files:

1. Documentation (PDF): A report with a strict 2,000-word maximum.
2. Video (MP4): A 5-minute maximum video screencast demonstrating your project's functionality.
3. Appendices (PDF): A separate file containing your full, unabridged source code.

7. The Extended Essay (EE) in Computer Science

The EE is a 4,000-word independent research paper. The new guidelines have formalized two distinct research pathways:

• Subject-Focused Pathway: A deep dive into a purely technical CS topic. This is perfect for comparing algorithm efficiencies or analyzing hardware performance. For example: "How does the time complexity of inserting values compare between a Scapegoat Tree and an AVL Tree's rebalancing algorithm?"
• Interdisciplinary Pathway: This combines Computer Science with another IB subject to answer a complex question. The integration must be essential, not just two topics side-by-side. For example: "To what extent can computer vision and convolutional neural networks be used to identify and translate American Sign Language?" (CS + Linguistics).

8. Beyond the Syllabus: TOK & The Collaborative Project

Theory of Knowledge (TOK) Connections

The new syllabus, especially the Machine Learning unit, is a goldmine for TOK. You can explore fascinating Knowledge Questions (KQs) for your exhibition or essay, such as: "How do ethical considerations in AI development challenge our understanding of what constitutes 'knowledge'?"

The Collaborative Sciences Project (CSP)

This mandatory 10-hour project replaces the old "Group 4 Project." You will team up with students from other Group 4 sciences (Biology, Chemistry, Physics) to tackle a real-world problem. As a CS student, you might build data visualizations or program sensors while a biology student analyzes the ecological data.

9. Your 2-Year Game Plan

A structured timeline is essential for success. Here is a recommended plan for your two-year course.

10. The Lanterna Edge: Secrets to Scoring a 7

Top students don't just know the content; they have a strategy. Here's how to get ahead:

• Master the Exams: Use a "Two-Pass Rule." In the exam, answer all the easy definition and syntax questions first. Then, circle back to the harder, multi-step algorithmic problems. This secures your marks and builds confidence.
• Execute a Flawless IA: Remember that examiners grade your PDF report, not just your code. The video is only for proof. Treat your IA like a technical writing assignment. Document everything, justify your choices, and connect every decision back to your success criteria.
• Conquer the Case Study: The case study booklet is a starting point, not the whole story. Spend months mapping the scenario to Theme A concepts. If the case study mentions processing power, research GPU architecture. If it mentions copyright, research data scraping laws. Structure your final answers like mini-essays: explain the tech, evaluate its impact, and critique the ethics.