Teaching Machine Learning Fundamentals with LEGO Robotics

arXiv cs.RO / 4/7/2026

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Key Points

  • The paper introduces “Machine Learning with Bricks,” a web-based, programming-free learning platform that uses LEGO robotics to teach machine learning to ages 12–17.
  • The curriculum covers three core algorithms—KNN, linear regression, and Q-learning—through activities where students collect data, train models, and control robots via a browser interface.
  • Interactive visualizations and guided video tutorials are designed to help learners connect algorithmic concepts to real robot behaviors without requiring coding.
  • Results from pre- and post-surveys with 14 students show statistically significant gains in self-reported understanding, improved use of technical AI terminology, high perceived usability, and increased motivation.
  • The authors argue that tangible, visualization-based teaching methods can make machine learning more accessible for young learners while still preserving technical depth, and they provide the platform openly online.

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 indicate statistically significant improvements in self-reported understanding of machine learning algorithms, changes in AI-related terminology toward more technical language, high platform usability, and increased motivation for continued learning. This work suggests 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.

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