Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
Sina Rismanchian, Hasan Uzun, Jeffrey Matayoshi, Eric Cosyn, Eyad Kurd-Misto

TL;DR
This study analyzes ten years of educational data to assess how generative AI like ChatGPT has reduced students' time on math problems and impacted their learning outcomes across different age groups.
Contribution
It provides large-scale behavioral and outcome evidence of AI's influence on student study habits and knowledge acquisition, using a quasi-experimental design and extensive data analysis.
Findings
AI use reduces time on AI-susceptible problems by up to 31% across age groups.
Proctoring eliminates efficiency gains, indicating the effect is due to AI use rather than other factors.
AI use correlates with a 25% decline in correct responses on non-proctored assessments.
Abstract
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 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 per quarter among college students after…
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