Meaningful Computer Science and AI Learning from LEGO® Education engages all students with hands-on collaboration at the heart of every experience. The extensive curriculum delivers impactful and relevant computer science and AI instruction with hands-on, scaffolded lessons that engage students in experiences connected to their world. The enquiry-based, curriculum-aligned lessons drives learning and empower students and teachers with any experience level to explore computer science.
The new Coding palette and starter code tailored for each lesson provides an intuitive student interface with word or icon-based block coding and is accessible via web browser or iOS app. The 30 hands-on computer science lessons grouped into six units, each including four lessons and a design challenge are accessible through the Teacher portal. The Teacher Portal empowers CS and AI beginners and experts alike with easy-to-teach, 45-minute, curriculum-aligned lessons that develop students’ AI literacy skills so students can understand, apply, and evaluate the technology.

Key Learning Outcomes:

Basics: Students bring creations to life by developing sequences and pseudocode to decompose and solve problems.

Hardware: Students learn to describe how hardware works, exploring the relationship between inputs and outputs.

Events: Students learn about events, which are actions that cause something to happen any time they are true.

Sequences: Students learn about sequences, which are sets of steps in a specific order to complete tasks.

Loops: Students are introduced to loops, which are used to repeat a sequence of code multiple times.

Conditionals: Students learn about conditionals, which perform different actions depending on whether conditions are true or false.

Variables: Students are introduced to variables, which are used to store, retrieve and interact with data.

Functions: Students learn about functions, which are coding sections that perform specific tasks, to design modular programs.

AI & data: Students engage with AI classification to explore how computers can learn from data.