Teaching Machine Learning Fundamentals with LEGO Robotics

TL;DR

The paper addresses the challenge of teaching core machine learning concepts to K-12 students without programming by introducing the open-source web platform Machine Learning with Bricks, which combines interactive visualizations with LEGO SPIKE Prime robotics to teach KNN, linear regression, and Q-learning. The authors design a two-day, no-code workshop and evaluate it with 14 participants, reporting significant improvements in conceptual understanding, shifts toward more technical AI terminology, high platform usability, and increased motivation for AI careers. Key contributions include a multi-algorithm, tangible visualization approach using off-the-shelf hardware, an accessible course structure, and empirical evidence supporting the effectiveness of hands-on, visualization-based robotics in early AI education. The work demonstrates that tangible, visualization-driven pedagogy can make ML concepts accessible and engaging, with implications for scalable, inclusive AI literacy in schools and future curriculum development.

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.

Figures (8)

• Figure 1: Landing page of the Machine Learning with Bricks platform showing the available experiments.
• Figure 2: Fruit detector consisting of a hub, a color sensor, and a caliper mechanism equipped with a distance sensor.
• Figure 3: User interface of the Fruit Detector experiment in inference mode. The 2D plot visualizes collected samples and the decision boundary. The interface also includes a data table for editing or deleting samples, and controls for switching between training and inference modes and adjusting the number of neighbors participating in the vote.
• Figure 4: Pitcher consisting of a hub, a distance sensor, and motors that, through a gear set, accelerate a pitching arm to launch a table tennis ball toward a target.
• Figure 5: User interface of the Pitcher experiment in inference mode. The 2D plot visualizes collected data points and the fitted regression line. The interface includes a data table for managing measurements and controls for adjusting line parameters, launching the ball, measuring distance, switching between training and inference modes, and calculating the best-fit line automatically.