Children's Mental Models of AI Reasoning: Implications for AI Literacy Education

TL;DR

This study explores how children understand AI reasoning, identifying three mental models and age-related differences, to inform AI literacy education and explainable AI design.

Contribution

It introduces a novel two-phase methodology to uncover children's mental models of AI reasoning, revealing developmental differences and educational implications.

Findings

Younger children see AI as inherently intelligent.

Older children recognize AI as pattern recognition.

Three tensions in children's understanding of AI reasoning.

Abstract

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.