Project title

Tracing Analogical Reasoning Development in Children and AI-models

Research question

How can insights from the developmental stages of children’s analogical reasoning be used to guide and improve the Abstraction, Broad Generalization, and Composition processes in AI models?

Project description

Claire Stevenson observes that, while analogical reasoning has been widely studied in human development and is only just beginning to emerge in large language models (LLMs), little is known about how AI acquires this skill.

Her research explores the development of analogical reasoning in both children and AI, as illustrated by puzzles such as “horse is to stable as chicken is to …?” She focuses particularly on the shift from associative thinking—for instance, “chicken–egg”—to relational reasoning, such as understanding “lives in.”

Whereas children typically make this transition between the ages of four and eight, AI models tend to exhibit early associative patterns but encounter difficulties with abstraction, generalisation, and compositional thinking.

By charting these developmental stages and comparing the trajectories of humans and machines, her study aims to both enhance AI reasoning and clarify whether models truly reason or merely reproduce associations and memorised patterns.

Selected publications

  • E. Stevenson and M. Hickendorff. Learning to solve figural matrix analogies: The paths children take. Learning and Individual Differences, 66:16-28, 2018.
  • Johnson, M. ter Veen, R. Choenni, H.L.J. van der Maas, E. Shutova, and C. E. Stevenson. 2025. Do large language models solve verbal analogies like children do?. In Proceedings of the 29th Conference on Computational Natural Language Learning, pages 627–639, Vienna, Austria. Association for Computational Linguistics.
  • C. E Stevenson, C. E Bergwerff, W. J. Heiser, and W. C. M. Resing. Working memory and dynamic measures of analogical reasoning as predictors of children’s math and reading achievement. Infant and Child Development, 23(1):51–66, 2014.
  • C. E Stevenson. Role of working memory and strategy-use in feedback effects on children’s progression in analogy solving: An explanatory item response theory account. International Journal of Artificial Intelligence in Education, 27(3):393–418, 2017.
  • H. L. J. Van Der Maas, K.-J. Kan, M. Marsman, and C. E. Stevenson. Network models for cognitive development and intelligence. Journal of Intelligence, 5(2):16, 2017.