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

arXiv: https://arxiv.org/abs/2505.16031 · HTML: https://mlsanity.com/paper/2505.16031/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 13 of its 106 references that are in the map.
- First posted on arXiv 21 May 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 13 of 13)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain](https://mlsanity.com/paper/2305.07141/) (2023)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI](https://mlsanity.com/paper/2406.12753/) (2024)
- [Reasoning Language Models: A Blueprint](https://mlsanity.com/paper/2501.11223/) (2025)
- [From Explainable to Interactive AI: A Literature Review on Current Trends in Human-AI Interaction](https://mlsanity.com/paper/2405.15051/) (2024)
- ["Alexa doesn't have that many feelings": Children's understanding of AI through interactions with smart speakers in their homes](https://mlsanity.com/paper/2305.05597/) (2023)
- ["Alexa, Can I Program You?": Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa](https://mlsanity.com/paper/2102.01367/) (2021)
- [Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI](https://mlsanity.com/paper/2408.01959/) (2024)
- [Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study](https://mlsanity.com/paper/2408.01961/) (2024)
- [Using Scratch to Teach Undergraduate Students' Skills on Artificial Intelligence](https://mlsanity.com/paper/1904.00296/) (2019)

## Built on by (most cited first, 5 of 5)

- [What is Safety? Corporate Discourse, Power, and the Politics of Generative AI Safety](https://mlsanity.com/paper/2602.06981/) (2026)
- [Toys that listen, talk, and play: Understanding Children's Sensemaking and Interactions with AI Toys](https://mlsanity.com/paper/2604.02629/) (2026)
- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)
- [AI Hallucination from Students' Perspective: A Thematic Analysis](https://mlsanity.com/paper/2602.17671/) (2026)
- [Where Does AI Leave a Footprint? Children's Reasoning About AI's Environmental Costs](https://mlsanity.com/paper/2603.27376/) (2026)

## Abstract (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 "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.