Assessment hub
Internal Assessment
Criteria A–E, course checkpoints, word-count guidance, video/appendix requirements, authenticity and final submission checklist.
Open IA hub →IB Computer Science HL · Year 2
Interactive Lesson Library
55 self-contained lessons for first assessment 2027. Open lessons online or download individual lessons for offline study.
Paper 1 Section B
2027 Case Study
Generative AI image creation: four HL challenge areas, terminology, research timeline and evidence-bank checklist.
Open Case Study hub →Your progress0 / 55 complete
Programming & Computational Thinking
Lessons 1–10Lesson 1
Computational-thinking retrieval + static/dynamic structures, arrays and lists
B1.1.1B1.1.2B1.1.3B2.1.1B2.2.1B2.2.2
Lesson 2
Stacks: LIFO operations and trade-offs
B2.2.3
Lesson 3
Queues: FIFO operations and trade-offs
B2.2.4
Lesson 4
Sequence and selection
B2.3.1B2.3.2
Lesson 5
Loops, functions, modularization and scope
B2.1.2B2.3.3B2.3.4
Lesson 6
Flowchart/algorithmic-thinking retrieval + algorithm efficiency and Big O
B1.1.4B2.4.1
Lesson 7
Linear and binary search
B2.4.2
Lesson 8
Bubble and selection sort
B2.4.3
Lesson 9
Recursion: concepts, tracing and construction
B2.4.4B2.4.5
Lesson 10
Exception handling, debugging, file processing + B2 Paper 2 checkpoint
B2.1.3B2.1.4B2.5.1B2.1.1B2.1.2B2.2.1B2.2.2B2.2.3
Object-Oriented Programming
Lessons 11–20Lesson 11
OOP foundations + UML class design
B3.1.1B3.1.2
Lesson 12
Static/instance members, constructors and object creation
B3.1.3B3.1.4
Lesson 13
Encapsulation and information hiding
B3.1.5
Lesson 14
Single-class OOP applied design/programming
B3.1.1B3.1.2B3.1.3B3.1.4B3.1.5
Lesson 15
B3.1 cumulative Paper 2 checkpoint
B3.1.1B3.1.2B3.1.3B3.1.4B3.1.5
Lesson 16
Inheritance
B3.2.1
Lesson 17
Polymorphism
B3.2.2
Lesson 18
Abstraction and abstract classes
B3.2.3
Lesson 19
Aggregation vs composition
B3.2.4
Lesson 20
Design patterns + B3.2 checkpoint
B3.2.5B3.2.1B3.2.2B3.2.3B3.2.4
Abstract Data Types
Lessons 21–28Lesson 21
ADTs + linked-list evaluation
B4.1.1B4.1.2
Lesson 22
Singly linked lists
B4.1.3
Lesson 23
Doubly and circular linked lists
B4.1.3
Lesson 24
Linked-list operations and applications
B4.1.2B4.1.3
Lesson 25
Binary search trees: structure and insertion
B4.1.4
Lesson 26
BST operations, traversal and evaluation
B4.1.4
Lesson 27
Sets as ADTs
B4.1.5
Lesson 28
Hash tables + B4 cumulative Paper 2 checkpoint
B4.1.6B4.1.1B4.1.2B4.1.3B4.1.4B4.1.5
Computer Fundamentals
Lessons 29–37Lesson 29
CPU architecture, memory and fetch-decode-execute
A1.1.1A1.1.4A1.1.5
Lesson 30
Secondary storage, compression and cloud services
A1.1.7A1.1.8A1.1.9
Lesson 31
Binary/hex and binary data representation
A1.2.1A1.2.2
Lesson 32
Logic gates, truth tables, Karnaugh maps and logic diagrams
A1.2.3A1.2.4A1.2.5
Lesson 33
Operating systems: roles, functions and scheduling
A1.3.1A1.3.2A1.3.3
Lesson 34
Polling/interrupts, multitasking and resource allocation
A1.3.4A1.3.5
Lesson 35
Control systems
A1.3.6A1.3.7
Lesson 36
Compilers, interpreters, JIT and bytecode
A1.4.1
Lesson 37
CPU/GPU, pipelining + A1 Paper 1 synthesis
A1.1.2A1.1.3A1.1.6A1.1.1A1.1.4A1.1.5A1.1.7A1.1.8
Networks
Lessons 38–42Lesson 38
Network purpose, types and digital infrastructures
A2.1.1A2.1.2
Lesson 39
Network devices, protocols and TCP/IP model
A2.1.3A2.1.4A2.1.5
Lesson 40
Topologies, servers, network models and segmentation
A2.2.1A2.2.2A2.2.3A2.2.4
Lesson 41
IP addressing, media, packet switching and routing
A2.3.1A2.3.2A2.3.3A2.3.4
Lesson 42
Network security + A2 Paper 1 synthesis
A2.4.1A2.4.2A2.4.3A2.4.4A2.1.1A2.1.2A2.1.3A2.1.4
Databases
Lessons 43–49Lesson 43
Relational databases + database schemas
A3.1.1A3.2.1
Lesson 44
ERDs, data types and relational table construction
A3.2.2A3.2.3A3.2.4
Lesson 45
Normalization to 3NF and denormalization
A3.2.5A3.2.6A3.2.7
Lesson 46
SQL languages, queries and updates
A3.3.1A3.3.2A3.3.3
Lesson 47
Aggregates, views and ACID transactions
A3.3.4A3.3.5A3.3.6
Lesson 48
Alternative databases + data warehouses
A3.4.1A3.4.2
Lesson 49
OLAP, data mining and distributed databases + A3 synthesis
A3.4.3A3.4.4A3.1.1A3.2.1A3.2.2A3.2.3A3.2.4A3.2.5
Machine Learning & 2027 Case Study
Lessons 50–55Lesson 50
Machine learning types, applications and hardware
A4.1.1A4.1.2
Lesson 51
Data preprocessing: cleaning, feature selection, dimensionality reduction
A4.2.1A4.2.2A4.2.3
Lesson 52
Supervised learning: regression, classification and evaluation
A4.3.1A4.3.2A4.3.3
Lesson 53
Clustering, association rules, reinforcement learning and genetic algorithms
A4.3.4A4.3.5A4.3.6A4.3.7
Lesson 54
ANNs, CNNs and model selection
A4.3.8A4.3.9A4.3.10
Lesson 55
Ethics retrieval + 2027 case study synthesis + final Paper 1 consolidation
A4.4.1A4.4.2A4.1.1A4.1.2A4.2.1A4.2.2A4.2.3A4.3.1