用数学思维教小学生理解AI本质,破除工具迷思。
A Structured Unplugged Approach for Foundational AI Literacy in Primary Education
- 以数学概念为基构建AI认知框架,强化数据与推理理解。
- 31名五年级学生测试后术语掌握与逻辑判断显著提升。
- 活动贴近生活场景,适合中小学AI启蒙教学参考。
新一代成长于智能技术主导的世界,早期人工智能素养对培养批判性理解与应对能力至关重要。然而当前教育多侧重工具使用,忽视底层概念,导致儿童易产生误解、期待过高,并难以识别偏见与刻板印象。本文提出一种结构化、可复现的教学方法,基于与小学数学课程紧密关联的核心概念,帮助学生建立对数据表示、分类推理及AI评估的深层理解。通过在两个班级共31名五年级学生中开展实证研究,结合后测与满意度调查,结果表明学生在术语理解、特征描述、逻辑推理和评价能力方面均有提升,对决策过程及其局限性的认知更加深入。教学活动广受学生欢迎,尤其青睐将AI概念与真实情境结合的任务。相关材料已开源:https://github.com/tail-unica/ai-literacy-primary-ed。
原文摘要 · Abstract (English)
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.
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