Adaptive AI tutoring platforms, e.g. Khanmigo, Duolingo Max, Squirrel AI, are increasingly pitched as the future of education. And the performance data for students using these tools is accordingly strong on paper when it comes to test scores and retention in controlled settings.
However, I recently came across a growing body of research, including this meta-analysis of 17 studies, suggesting that the gains actually concentrate at lower cognitive levels and tend to shrink or reverse for higher-order reasoning. The mechanism seems to be cognitive offloading: when the tool does the structuring, pattern recognition, and problem decomposition work, students perform better in the moment but engage less deeply with the material itself.
The issue is that the pitch for AI in education is fundamentally built on learning rather than performance on its own. If adaptive systems are producing better test results but also weakening the cognitive and analytical reasoning skills that constitute actual learning, the value proposition is arguably more nuanced than it suggests.
The research does hint at a potential design direction: cognitive gains that matter most seem to develop through effortful and creative engagement with a certain level of productive struggle. Thus, a more beneficial approach overall to AI-personalized learning might be platforms that seek to actively calibrate difficulty rather than remove it.
What's your take?
