[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85599-en":3,"doc-seo-85599-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85599,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build","Examines how generative AI reshapes students’ study behavior and durable learning outcomes after ChatGPT, reconciling survey stability with behavioral evidence. Uses a ten-year panel of 3.2 million ALEKS learning interactions plus 12.2 million ALEKS placement-assessment response times. Finds time on AI-susceptible text word problems declines after ChatGPT, while the post-ChatGPT divergence disappears under proctoring and retention odds of correct response drop, indicating a durable learning cost. Implications cover education research, assessment governance, and AI policy.","Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build  \narXiv :2605 .21629v3 [ cs .CY] 13 Jul 2026  \nSina Rismanchian∗ University of California, Irvine [srismanc@uci.edu](srismanc@uci.edu)  \nHasan Uzun∗  \nMcGraw Hill [hasan.uzun@mheducation.com](hasan.uzun@mheducation.com)  \nJeffrey Matayoshi  \nMcGraw Hill  \n[jeffrey.matayoshi@mheducation.com](jeffrey.matayoshi@mheducation.com)  \nEric Cosyn  \nMcGraw Hill  \n[eric.cosyn@mheducation.com](eric.cosyn@mheducation.com)  \nEyad Kurd-Misto  \nMcGraw Hill  \n[eyad.kurd-misto@mheducation.com](eyad.kurd-misto@mheducation.com)  \nAbstract  \nHow 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-ontask, complemented by 12.2 million ALEKS PPL placement-assessment response times for examining proctoring and learning outcomes, with a quasi-experimental design exploiting variation in tasks that are more susceptible to AI (text-based word problems) and less susceptible to AI (interactive graph-based problems) . Learning time on AIsusceptible 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%, middleschoolers 9.0%, and Grade 5 students no detectable change. Among college students, the post-ChatGPT divergence vanishes entirely under proctoring, ruling out broad efficiency gains as the likely explanation. 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 populationlevel indicator of cognitive surrender, with direct implications for educational research, assessment governance, and AI policy.  \nSignificance Statement  \nWhether generative AI has changed how students study and what they learn has been hard to answer: surveys show little change, and small experiments cannot detect longitudinal shifts. Using 3.2 million mathematics learning interactions over a decade, we document a large post-ChatGPT decline in time spent on AI-susceptible problems which is absent under proctoring. The shift carries a durable learning cost: on randomly assigned proctored retention items, odds of correct response fall by 25%, while the same estimator on non-proctored assessment yields a large opposite-signed increase—a reversal that is unlikely for non-AI mechanism to produce.  \n∗ Equal contribution; [co-first authors. Corresponding author:](co-first authors. Corresponding author: srismanc@uci.edu)[ srismanc@uci.edu](co-first authors. Corresponding author: srismanc@uci.edu).  \n1 Introduction  \nThe arrival of ChatGPT and generative AI chatbots (Reich & Dukes, 2025) placed a capable school-level mathematics problem-solver (Bubeck et al., 2023) in the hands of millions of students with essentially zero marginal cost. It is widely assumed that this shift has changed how students study, nonetheless, reliable evidence on how much student behavior has actually moved is scarce. Large-scale self-report surveys of secondary and postsecondary students in the years following ChatGPT’s release find little to no change in the rate at which high school students report using AI for academically dishonest behaviors relative to pre-ChatGPT baselines (Lee et al., 2024; Chen et al., 2026) . This stability is at odds with wides","cbCaidgO0TJDQt7y","https://ap.wps.com/l/cbCaidgO0TJDQt7y","pdf",633760,10,1,24,"English","en",105,"# Abstract\n# Significance Statement\n# Introduction","[{\"question\":\"What gap does the study address about generative AI’s impact on learning?\",\"answer\":\"The study addresses the mismatch between self-report surveys showing little change and the lack of large-scale behavioral evidence that can detect longitudinal shifts in study behavior and learning outcomes.\"},{\"question\":\"What data sources and time span are used to measure behavioral change?\",\"answer\":\"It uses a ten-year panel from ALEKS: 3.2 million learning interactions for time-on-task and 12.2 million placement-assessment response-time observations for proctoring and learning outcomes.\"},{\"question\":\"How do the results differ between AI-susceptible and less AI-susceptible math problem types?\",\"answer\":\"Learning time declines for AI-susceptible text-based word problems after ChatGPT’s release, while the divergence is absent under proctoring, suggesting the change is tied to unsupervised AI-assisted work and not broad efficiency 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gap does the study address about generative AI’s impact on learning?","Question",{"text":76,"@type":77},"The study addresses the mismatch between self-report surveys showing little change and the lack of large-scale behavioral evidence that can detect longitudinal shifts in study behavior and learning outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources and time span are used to measure behavioral change?",{"text":81,"@type":77},"It uses a ten-year panel from ALEKS: 3.2 million learning interactions for time-on-task and 12.2 million placement-assessment response-time observations for proctoring and learning outcomes.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the results differ between AI-susceptible and less AI-susceptible math problem types?",{"text":85,"@type":77},"Learning time declines for AI-susceptible text-based word problems after ChatGPT’s release, while the divergence is absent under proctoring, suggesting the change is tied to 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