Children's Mental Models of AI Reasoning: Implications for AI Literacy Education
This research addresses the problem of AI literacy education for children by providing insights into their understanding of AI reasoning, which is incremental as it builds on existing educational frameworks.
The study investigated children's mental models of AI reasoning, identifying three models (Deductive, Inductive, and Inherent) through co-design with 8 children and a field study with 106 children in grades 3-8, finding that younger children often attribute AI reasoning to inherent intelligence while older children see it as pattern recognition.
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.