Generative AI reduced study time on AI-susceptible math problems by 9-31% across grade levels and produced a 25% decline in retention odds on proctored assessments.
Ruishi Chen, Victor R Lee, Annie Camey Kuo, Denise Clark Pope, and Sarah Miles
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VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
Using distribution regression on Consumption Expenditure Interview Survey data, the study decomposes the 2018-2022 decline in consumption inequality into contributions from conditional consumption distributions, rising asset holdings, and household characteristics for male-headed households.
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Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build
Generative AI reduced study time on AI-susceptible math problems by 9-31% across grade levels and produced a 25% decline in retention odds on proctored assessments.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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Distributional Decomposition of Consumption Inequality Change During COVID-19
Using distribution regression on Consumption Expenditure Interview Survey data, the study decomposes the 2018-2022 decline in consumption inequality into contributions from conditional consumption distributions, rising asset holdings, and household characteristics for male-headed households.