The Promise of AI Object-Recognition in Learning Mathematics: An Explorative Study of 6-Year-Old Children’s Interactions with Cuisenaire Rods and the Blockplay.ai App

Abstract

We developed and trained a prototype AI-based object-recognition app, blockplay.ai, to recognise Cuisenaire rods placed on a tabletop, and speak the rods’ lengths. We challenged 6-year-olds in a primary school in England to play a ‘game’: can you make the app say the two times table? Drawing methodologically on theories of embodiment, we analyse two videoclips, each of a child interacting with rods, the app and the task set by the researchers, as a dynamic, complex child-rods-app-task body-artefact system. Theoretically we draw on Davydovian concepts of learning as a concrete-to-abstract-to-new-concrete cycle, using abstract artefacts such as mathematical language to coordinate new perceptually-guided actions on concrete objects. In one videoclip the child’s pattern of actions are consistent with a change, within a few minutes, from perceiving and acting on rods as counters, to perceiving and acting on rods as lengths; in the other videoclip, this does not happen. We analyse the changes in patterns of interactions as shifts to new stable attractors in a dynamic child-rods-app-task body-artefact system, driven by tensions generated by unexpected concrete-to-abstract relationships. We end by looking forward to the range of possible uses of object-recognition technology in the learning of mathematics, for example, provoking algebraic awareness.