Teaching Machine Learning Fundamentals with LEGO Robotics

2026-01-27

AI Summary

This work addresses the challenge of teaching machine learning to adolescents aged 12–17 with no prior programming experience by proposing and implementing an open-source web platform, “Machine Learning with Bricks.” The platform innovatively integrates embodied learning with interactive visualizations, leveraging hands-on activities driven by LEGO robots to teach three core algorithms—k-nearest neighbors (KNN), linear regression, and Q-learning—without requiring any coding. While preserving technical depth, the approach substantially lowers cognitive barriers to entry. A pre- and post-test evaluation involving 14 students demonstrated significant improvements in learners’ conceptual understanding of machine learning, increased interest and motivation toward AI, and high platform usability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Embodied AI
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day course designed to teach machine learning concepts to students aged 12 to 17 through programming-free robotics activities. Machine Learning with Bricks is an open source platform and combines interactive visualizations with LEGO robotics to teach three core algorithms: KNN, linear regression, and Q-learning. Students learn by collecting data, training models, and interacting with robots via a web-based interface. Pre- and post-surveys with 14 students demonstrate significant improvements in conceptual understanding of machine learning algorithms, positive shifts in AI perception, high platform usability, and increased motivation for continued learning. This work demonstrates that tangible, visualization-based approaches can make machine learning concepts accessible and engaging for young learners while maintaining technical depth. The platform is freely available at https://learning-and-dynamics.github.io/ml-with-bricks/, with video tutorials guiding students through the experiments at https://youtube.com/playlist?list=PLx1grFu4zAcwfKKJZ1Ux4LwRqaePCOA2J.

Problem

• machine learning education
• K-12 AI literacy
• programming-free learning
• tangible learning
• LEGO robotics

Innovation

• LEGO robotics
• programming-free ML education
• interactive visualization
• tangible learning
• KNN and Q-learning