Razumevanje delovanja umetne inteligence je od matematike sedmega razreda oddaljeno največ 37 konceptov

Learning space is a formal mathematical structure that enables modeling of a learning process. Knowledge structure consists of items of information that the individual learns, and the states of knowledge, in which the individual finds himself when he acquires knowledge. Items of information and states of knowledge respect the axiom of accessibility and learning consistency. A path in the learning space represents a walk from the state, where the individual does not know any of the items, to the state in which the individual acquires all the items of information. In this paper, we analyze the learning space of seventh-grade mathematics and the learning space of a research paper, in which a seventh grader presented the reinforcement learning algorithm of the game Nim at a level accessible to elementary school students. Based on the analysis, we evaluate that reinforcement learning is 37 items of information away from seventh grade math to understand, not accounting for the concepts required to program it in Python. We complete the analysis with suggestions of further research on introducing artificial intelligence in primary and secondary schools.