Fostering machine learning literacy in senior primary education: Evaluating a structured pedagogical course design

Current K–12 artificial intelligence (AI) literacy education emphasizes tool usage over fundamental concepts, yet AI literacy requires grasping how and why AI works – critical for an AI-driven society. This highlights the need for machine learning (ML) education for young learners. We designed and evaluated a six-to 8-h ML course for 752 senior primary students (average age 11.36) across seven Hong Kong primary schools. Pre- and post-test results showed significant improvement in ML understanding, with a Wilcoxon effect size of 0.55. Students comprehended supervised learning and reinforcement learning, including algorithms such as k-nearest neighbours and artificial neural networks, via training robots in competitive circuit tasks and real-time algorithm visualization. Thematic analysis of student interviews revealed that our structured pedagogical approach — blending guided worksheets, hands-on activities, and iterative refinement of data processing, parameter adjustment, and model training within the robots' software — enhanced students’ conceptual learning and engagement. Surprisingly, they developed initial reflections on distinguishing between AI and human learning. These findings suggest the feasibility and promise of teaching fundamental ML concepts to senior primary students through a structured course design. The study contributes to future research and practice in fostering ML literacy among young learners, providing actionable insights for educators to support and allocate resources.