Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
arXiv:2605.21629v2 Announce Type: replace-cross 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 $3.2$ million ALEKS learning interactions for investigating time-on-task, complemented by ALEKS PPL placement-assessment data for examining proctoring and learning outcomes, wi
This study is among the first to use long-term, large-scale behavioral data to quantify the impact of generative AI on learning outcomes, moving beyond self-report surveys and small-scale experiments.
This research provides empirical evidence that generative AI, while potentially increasing completion speed, can significantly degrade the depth and retention of learned knowledge, indicating a fundamental challenge to education and skill development.
The understanding of generative AI's impact on education shifts from speculation and self-reporting to data-driven insights about reduced long-term knowledge acquisition, which could necessitate reevaluating pedagogical approaches and AI integration strategies.
- · AI-powered assessment tools
- · Educational platforms focusing on conceptual understanding
- · Human tutors and specialized educators
- · AI ethics and policy researchers
- · Generative AI tools marketed solely for task completion
- · Traditional rote learning pedagogy
- · Students relying excessively on AI for quick answers
- · Education systems slow to adapt to AI's impact on learning
Students using generative AI complete tasks faster but acquire less durable knowledge in mathematics.
Educational institutions will face pressure to adapt curricula and assessment methods to mitigate the negative impact of AI on deep learning, or risk producing less competent graduates.
A future workforce with diminished foundational knowledge in critical areas could lead to a 'skills gap' and a need for extensive reskilling and upskilling programs later in careers.
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Read at arXiv cs.AI