Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build

How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes? Self-report surveys show little change, while small-scale behavioral studies report widespread AI use without the scale or duration to measure learning consequences. We address both questions using a ten-year panel of $3.2$ million ALEKS learning interactions for the time-on-task analysis, complemented by ALEKS PPL placement-assessment data for the proctoring and retention analyses, with a quasi-experimental design exploiting within-curriculum variation in AI susceptibility: text-based word problems transcribable into AI prompts serve as the treated group; graph-based problems requiring interactive platform manipulation as the comparison. Learning time on AI-susceptible problems declines $2.8\%$ per quarter among college students after ChatGPT's release, cumulating to $26.9\%$ over eleven quarters; high-schoolers show $31.3\%$, middle-schoolers $9.0\%$, and Grade 5 students no detectable change. The divergence vanishes entirely under proctoring for college students, making general efficiency gains unlikely. Logistic fixed-effects models on randomly assigned proctored retention items yield a $25\%$ cumulative decline in odds of correct response; the same estimator on non-proctored assessment produces a large opposite-signed increase -- inconsistent with any platform, cohort, or curriculum explanation. These results are among the first large-scale behavioral and outcome evidence that generative AI has altered how students study and the knowledge they build -- the population-level indicator of \emph{cognitive surrender}, with direct implications for educational research, assessment governance, and AI policy.