Date
Publisher
arXiv
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 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.
Study at a glance
- Question: How do children understand how artificial intelligence reasons?
- Setting and data: 8 children in a co-design session and 106 children in grades 3–8 in a field study
- Method: Two-phase approach featuring a co-design session followed by a field study
Why it matters
As artificial intelligence advances in reasoning capabilities, understanding how children conceptualize AI reasoning processes is critical for fostering AI literacy. However, less is known about children's mental models of the reasoning algorithms central to AI decision-making.
Key findings
- Among 106 children in grades 3–8, students conceptualized AI reasoning through three distinct mental models: Deductive, Inductive, and Inherent.
- Younger children in grades 3–5 often attribute AI reasoning to inherent intelligence, while older children in grades 6–8 recognize AI as a pattern recognizer.
- Three key tensions surface in children's understanding of how artificial intelligence reasons.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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