Augmented Thinking: How AI Assistance Reshapes Human Reasoning in Problem-Solving

As AI-driven tutoring systems become ubiquitous in education, they offer real-time guidance andpersonalized feedback, yet the cognitive consequences of this interaction remain largely unexamined.This study investigates how varying levels of AI assistance reshape not only task performance, but thevery structure of human reasoning.Using a quantitative computational approach, I analyzed open-source educational interaction datasetsacross three conditions: independent problem-solving, hint-supported assistance, and fully-guided AIcompletion.Key metrics included task efficiency, accuracy, reasoning step frequency, and explanation complexity.Machine Learning models (logistic regression) were applied to assess predictive relationships between AIassistance and cognitive engagement.Results indicate that unless higher AI support significantly improves efficiency and accuracy, it correlateswith a marked reduction in participant-generated reasoning steps and explanatory depth. Hint-supportedconditions preserved learner-driven cognitive activity more effectively than fully automated guidance.These findings suggest a redistribution, not simply enhancement, of cognitive effort under algorithmicinfluence.This research contribute to the emerging framework of augmented thinking, emphasizing the need forethically designed AI that supports both performance and metacognitive growth. Grounded in my ownexperience as a self-taught student in a resource-limited environment, this study reflects a broaderquestion: not just what technology is able to do, but what it does to us, to our mind. The implicationsextend to educational equity, learner autonomy, and the design of AI systems that nurture, rather thanreplace, human thought.