Date
Publisher
arXiv
This paper presents an innovative pedagogical framework employing tangible
interactive games to enhance artificial intelligence (AI) knowledge and
literacy among elementary education students. Recognizing the growing
importance of AI competencies in the 21st century, this study addresses the
critical need for age-appropriate, experiential learning tools that demystify
core AI concepts for young learners. The proposed approach integrates physical
role-playing activities that embody fundamental AI principles, including neural
networks, decision-making, machine learning, and pattern recognition. Through
carefully designed game mechanics, students actively engage in collaborative
problem solving, fostering deeper conceptual understanding and critical
thinking skills. The framework further supports educators by providing detailed
guidance on implementation and pedagogical objectives, thus facilitating
effective AI education in early childhood settings. Empirical insights and
theoretical grounding demonstrate the potential of tangible interactive games
to bridge the gap between abstract AI theories and practical comprehension,
ultimately promoting AI literacy at foundational educational levels. The study
contributes to the growing discourse on AI education by offering scalable and
adaptable strategies that align with contemporary curricular demands and
prepare young learners for a technologically driven future.
Study at a glance
- Question: How can physical, role-playing games help elementary students learn core concepts of artificial intelligence without digital devices?
- Setting and data: Elementary school classrooms; no specific location, timeframe, or participant counts stated in the paper.
- Method: Pedagogical framework design outlining physical classroom games and learning activities.
Why it matters
Traditional AI education relies on digital tools and abstract concepts that may not engage young learners. Physical, screen-free games provide an accessible way for primary teachers without AI backgrounds to teach core computing ideas.
Key findings
- A classroom neural network game uses colored ropes and t-shirts to demonstrate weighted inputs, thresholds, and iterative error correction through physical role-play.
- A surprise box game introduces uncertainty, expected value, and the cost of acquiring probabilistic information through tangible choices.
- The Little Trainers, Predictors, and Classroom Spotify activities illustrate supervised learning, pattern classification, and music recommender systems using everyday materials without technology.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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