Fast spread of smart computer programs, especially big language models, changed how people learn today - offering quick answers, short recaps, because they help solve problems fast. Even though such tools bring clear advantages in education, there’s a risk learners might feel they know more than they actually do. What looks like solid grasp could just be surface-level comfort. This work digs into how thinking about one's own thinking connects to overconfidence when using AI helpers. Ideas around mental effort, memory recall, reliance on outside aids play a role here. Smoothness of machine-made replies often tricks students into believing they’ve learned deeply. That smooth flow gets mistaken for real insight, which leads them to rate their knowledge higher - even if test results tell another story. Using large language models too much might make people think less deeply, skip useful memory exercises, then forget things faster. What students believe they learn often does not match what they actually retain - a pattern showing how easily judgment can drift off track with AI help. Even so, artificial intelligence tools still hold value inside classrooms if paired with methods like questioning oneself or pausing to reflect. Clear guidance emerges near the end: those who study, teach, or build tech should adjust their habits to support sharper thinking alongside machines. This work adds weight to ongoing conversations about pairing smart devices with mental effort, aiming for deeper understanding that lasts longer.