The Risk of Early AI Adoption in Early Mathematics Education: Conditional Predictions for Students, Educators, and Parents

Children may soon learn mathematics with an AI assistant from their first year of school. What would that do to their ability to work alone? The Correction Freeze Problem (CFP), postulated and proved by Professor Vuong Quan Hoang, is a mathematical framework for how a self-checking system can stop noticing that it has changed. This paper asks what CFP would predict for this setting. It derives five predictions and states for each one what would count against it. CFP suggests that routine answer-giving could first appear as students not starting hard problems, not as slower work. Yearly checks could miss the change. Answer-first and hint-first instruction could diverge. Dependence, once set, could be harder to undo than to create. A scenario analysis and worst-case milestones for grades 5, 9 and 12 show what the model allows and what it does not. Since evidence comes only from short experiments with adults, and none involves children, the predictions are, therefore, conditional hypotheses. The paper names the measurements that would test them and a precautionary course of action.