孩子怎么理解AI的思考方式？研究揭示认知差异与教育启示。
Children's Mental Models of AI Reasoning: Implications for AI Literacy Education
- 通过儿童共创与实地调研，发现三种AI推理认知模型。
- 低年级孩子认为AI有天生智慧，高年级则视其为模式识别器。
- 结果为设计可解释AI工具和分龄教学提供依据。
随着人工智能推理能力的发展，尤其是大型推理模型（LRMs）的出现，理解儿童如何构想AI的推理过程对培养AI素养至关重要。尽管AI教育中的‘五大核心概念’之一强调推理算法是AI决策的核心，但关于儿童在该领域的心理模型仍知之甚少。本研究采用两阶段方法：首先与8名儿童进行共同设计，随后在106名3至8年级儿童中开展实地研究。结果识别出三种关于AI推理的认知模型：演绎型、归纳型与内在型。研究发现，低年级儿童（3-5年级）常将AI的推理归因于内在智能，而高年级儿童（6-8年级）更倾向于将其视为模式识别工具。研究还揭示了儿童理解中的三重张力，并据此提出对构建阶梯式AI课程与可解释性AI工具的设计建议。
原文摘要 · Abstract (English)
As artificial intelligence (AI) advances in reasoning capabilities, most recently with the emergence of Large Reasoning Models (LRMs), understanding how children conceptualize AI's reasoning processes becomes critical for fostering AI literacy. While one of the "Five Big Ideas" in AI education highlights reasoning algorithms as central to AI decision-making, less is known about children's mental models in this area. Through a two-phase approach, consisting of a co-design session with 8 children followed by a field study with 106 children (grades 3-8), we identified three models of AI reasoning: Deductive, Inductive, and Inherent. Our findings reveal that younger children (grades 3-5) often attribute AI's reasoning to inherent intelligence, while older children (grades 6-8) recognize AI as a pattern recognizer. We highlight three tensions that surfaced in children's understanding of AI reasoning and conclude with implications for scaffolding AI curricula and designing explainable AI tools.
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