A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
Maria Cristina Carrisi, Mirko Marras, Sara Vergallo

TL;DR
This paper introduces a structured, mathematically grounded teaching approach to improve foundational AI literacy among primary school students, emphasizing understanding over tool usage.
Contribution
It presents a novel, replicable curriculum that integrates core mathematical concepts to enhance AI understanding in young learners, supported by empirical evaluation.
Findings
Students showed improved AI terminology understanding.
Enhanced reasoning and evaluative skills observed.
Students found the activities engaging and relevant.
Abstract
Younger generations are growing up in a world increasingly shaped by intelligent technologies, making early AI literacy crucial for developing the skills to critically understand and navigate them. However, education in this field often emphasizes tool-based learning, prioritizing usage over understanding the underlying concepts. This lack of knowledge leaves non-experts, especially children, prone to misconceptions, unrealistic expectations, and difficulties in recognizing biases and stereotypes. In this paper, we propose a structured and replicable teaching approach that fosters foundational AI literacy in primary students, by building upon core mathematical elements closely connected to and of interest in primary curricula, to strengthen conceptualization, data representation, classification reasoning, and evaluation of AI. To assess the effectiveness of our approach, we conducted an…
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Taxonomy
TopicsOnline Learning and Analytics · Explainable Artificial Intelligence (XAI)
