Children's Mental Models of AI Reasoning: Implications for AI Literacy Education
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916751129706496 |
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| author | Dangol, Aayushi Wolfe, Robert Zhao, Runhua Kim, JaeWon Ramanan, Trushaa Davis, Katie Kientz, Julie A. |
| author_facet | Dangol, Aayushi Wolfe, Robert Zhao, Runhua Kim, JaeWon Ramanan, Trushaa Davis, Katie Kientz, Julie A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Children's Mental Models of AI Reasoning: Implications for AI Literacy Education Dangol, Aayushi Wolfe, Robert Zhao, Runhua Kim, JaeWon Ramanan, Trushaa Davis, Katie Kientz, Julie A. Artificial Intelligence Human-Computer Interaction 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. |
| title | Children's Mental Models of AI Reasoning: Implications for AI Literacy Education |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.16031 |