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 and personalized 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 the very structure of human reasoning. Using a quantitative computational approach, I analyzed open-source educational interaction datasets across three conditions: independent problem-solving, hint-supported assistance, and fully-guided AI completion. Key metrics included task efficiency, accuracy, reasoning step frequency, and explanation complexity. Machine Learning models (logistic regression) were applied to assess predictive relationships between AI assistance and cognitive engagement. Results indicate that unless higher AI support significantly improves efficiency and accuracy, it correlates with a marked reduction in participant-generated reasoning steps and explanatory depth. Hint-supported conditions preserved learner-driven cognitive activity more effectively than fully automated guidance. These findings suggest a redistribution, not simply enhancement, of cognitive effort under algorithmic influence. This research contribute to the emerging framework of augmented thinking, emphasizing the need for ethically designed AI that supports both performance and metacognitive growth. Grounded in my own experience as a self-taught student in a resource-limited environment, this study reflects a broader question: not just what technology is able to do, but what it does to us, to our mind. The implications extend to educational equity, learner autonomy, and the design of AI systems that nurture, rather than replace, human thought.