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A Structured Unplugged Approach for Foundational AI Literacy in Primary Education

Maria Cristina Carrisi, Mirko Marras, Sara Vergallo

TL;DR

This paper addresses the need for foundational AI literacy in primary education by proposing a structured unplugged learning path that integrates AI concepts with core mathematical reasoning. The curriculum consists of four modules—Introduction to AI, Classification Principles, Classification Representations, and Final Assessment & Reflection—delivered through hands-on, unplugged activities aligned with primary curricula. An empirical study with 31 fifth-grade students across two classes showed improvements in AI concept understanding, data representation, and reasoning about classification, along with positive engagement, especially when AI concepts were tied to real-world reasoning. The work provides a replicable framework and materials (e.g., a GitHub repository) to scale foundational AI literacy in early education, with implications for fostering critical citizenship and interdisciplinary learning that links AI with mathematics.

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 empirical study with thirty-one fifth-grade students across two classes, evaluating their progress through a post-test and a satisfaction survey. Our results indicate improvements in terminology understanding and usage, features description, logical reasoning, and evaluative skills, with students showing a deeper comprehension of decision-making processes and their limitations. Moreover, the approach proved engaging, with students particularly enjoying activities that linked AI concepts to real-world reasoning. Materials: https://github.com/tail-unica/ai-literacy-primary-ed.

A Structured Unplugged Approach for Foundational AI Literacy in Primary Education

TL;DR

This paper addresses the need for foundational AI literacy in primary education by proposing a structured unplugged learning path that integrates AI concepts with core mathematical reasoning. The curriculum consists of four modules—Introduction to AI, Classification Principles, Classification Representations, and Final Assessment & Reflection—delivered through hands-on, unplugged activities aligned with primary curricula. An empirical study with 31 fifth-grade students across two classes showed improvements in AI concept understanding, data representation, and reasoning about classification, along with positive engagement, especially when AI concepts were tied to real-world reasoning. The work provides a replicable framework and materials (e.g., a GitHub repository) to scale foundational AI literacy in early education, with implications for fostering critical citizenship and interdisciplinary learning that links AI with mathematics.

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 empirical study with thirty-one fifth-grade students across two classes, evaluating their progress through a post-test and a satisfaction survey. Our results indicate improvements in terminology understanding and usage, features description, logical reasoning, and evaluative skills, with students showing a deeper comprehension of decision-making processes and their limitations. Moreover, the approach proved engaging, with students particularly enjoying activities that linked AI concepts to real-world reasoning. Materials: https://github.com/tail-unica/ai-literacy-primary-ed.

Paper Structure

This paper contains 13 sections, 4 figures.

Table of Contents

  1. Introduction
  2. The Proposed Learning Path
  3. Module 1: Introduction to AI (2 hours)
  4. Module 2: Classification Principles (2 hours)
  5. Module 3: Classification Representations (2 hours)
  6. Module 4: Final Assessment & Reflection (2 hours)
  7. Experimental Evaluation
  8. Experimental Context
  9. Impact on AI Concepts Understanding [RQ1]
  10. Impact on Underlying Mathematical Skills [RQ2]
  11. Impact on Students' Engagement and Interest [RQ3]
  12. Discussion and Implications
  13. Conclusions and Future Work

Figures (4)

  • Figure 1: Collaborative classification task [Module 3].
  • Figure 2: [RQ1] Performance distribution on AI-related post-test exercises.
  • Figure 3: [RQ2] Performance distribution on math-related post-test exercises.
  • Figure 4: [RQ3] Student answers about perceptions of engagement.