[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85817-en":3,"doc-seo-85817-105":30,"detail-sidebar-cat-0-en-105":91},{"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},85817,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Learning Behavior Accounts for Background-related Advantage in AI-assisted Education","Generative AI is increasingly used in education, yet prior studies report inconsistent average effects that often conceal wide differences among student groups. This research investigates how learning behavior with AI varies by learner profiles and how those behavior patterns relate to learning outcomes. In structured experiments with 318 university students completing course tasks up to 125 minutes, proactive and critical engagement links strongly to higher performance. Students from higher-ranked universities and with stronger prior knowledge benefit more. Explaining results through learning behavior weakens or removes direct profile–outcome links, indicating AI usage as a key pathway.","arXiv :2607 . 10101v1 [ cs .HC] 11 Jul 2026  \nLearning behavior accounts for background-related advantage in AI-assisted education  \nJingwei Yi 1∗ , Yueqi Xie2,3†, Jiyan He1∗ , Rui Ye4 , Junming Huang3†, Bin Zhu5 , Sean Rintel6 , Yu Xie3,7 , Xing Xie8 , and Fangzhao Wu8†  \n1 University of Science and Technology of China, Hefei 230026, China  \n2 Hong Kong University of Science and Technology, Hong Kong  \n3 Paul and Marcia Center on Contemporary China, Princeton University, Princeton, NJ 08544, United States  \n4 Shanghai Jiao Tong University, Shanghai 200240, China  \n5 Microsoft AI Asia, Beijing 100080, China  \n6 Microsoft Research Cambridge, United Kingdom  \n7 Center for Social Research, Guanghua School of Management, Peking University, Beijing 100871, China  \n8 Microsoft Research Asia, Beijing 100080, China  \n†Correspondence: [yxieay@connect.ust.hk](yxieay@connect.ust.hk), [pub@junminghuang.com](pub@junminghuang.com), [fangzwu@microsoft.com](fangzwu@microsoft.com)  \n∗ Jingwei Yi and Jiyan He are MSRA Intern students.  \nABSTRACT  \nGenerative AI has been found, and will likely be found increasingly, useful in education. However, existing AI-for-education studies provide inconsistent evidence on its average effects. More broadly, research on prior educational technologies shows that average effects often mask substantial heterogeneity across student populations. Motivated by this evidence, this study examines heterogeneity in students’ learning behavior with AI, which students benefit from AI assistance, and how learner profiles and learning behavior shape these patterns. To this end, we recruited 318 university students to participate in structured learning experiments lasting up to 125 minutes. Our findings indicate that students’ learning behavior is strongly associated with learning outcomes, with behaviors characterized by proactive and critical engagement, rather than limited engagement, associated with significantly better performance. These behavioral differences are related to learner profiles, with students from higher-ranking universities and those with greater prior knowledge tending to benefit more, consistent with their greater likelihood of adopting proactive interaction strategies. Accounting for learning behavior substantially weakens or eliminates the associations between learner profiles and learning outcomes, suggesting that how students use AI is a key pathway through which background differences are linked to learning gains. Overall, this work provides a deeper understanding of AI assistance in education by showing how differences in learner profiles and learning behavior shape who benefits from AI-supported learning. These insights can help educators and students better navigate and integrate AI into educational practices.  \nC Learner profiles, behaviors and widened gap  \nLearner profiles More likely  \nLearning behaviors  \n\n|  | Higher university ranking / Stronger prior knowledge |  | Proactive and critical engagement |\n| --- | --- | --- | --- |\n\n|  |  | \u003Cbr> | Limited engagement |\n| --- | --- | --- | --- |\n\nMore likely  \nExamscore  \nLearning outcomes  \nWidened gap  \nLow Medium High  \nLearner profile level  \nFigure 1. Study design and conceptual framework linking AI-use learning behavior to heterogeneous learning outcomes. a, Study design and procedure. A total of 318 university students were assigned to one of two course contexts, Python or game theory, and randomly allocated within each course to either the experimental group with GPT access or the control group without LLM-based tool access. The procedure includes a pre-task survey, course learning, an assignment, review, an exam without GPT access for all participants, and a post-task survey. b, Conceptual overview of heterogeneous learning behavior during AI-assisted study. Student interactions with AI range from limited engagement, including abstention and rote-adoption of AI output, to proactive and critical engagement, including active-trial, error","cbCaijpfXtBLg6XH","https://ap.wps.com/l/cbCaijpfXtBLg6XH","pdf",4787724,3,1,53,"English","en",105,"# Abstract\n# Study Design and Conceptual Framework\n## Learner profiles and learning behaviors\n## Learning outcomes and widened gaps\n# Background: LLMs in Education\n## Student usage evidence\n## Debate and educational value","[{\"question\":\"What problem does the study address in AI-assisted education research?\",\"answer\":\"Existing AI-for-education studies show inconsistent average effects, while prior work on educational technologies suggests these averages hide heterogeneity across student groups. The study focuses on why some students benefit more than others when using AI for learning.\"},{\"question\":\"How was the study conducted to examine learning behavior and outcomes?\",\"answer\":\"The study recruited 318 university students for structured experiments lasting up to 125 minutes. Participants worked in two course contexts (Python or game theory), with students randomly assigned to an experimental group with GPT access or a control group without LLM tool access, including pre- and post-task surveys and an exam without GPT access for all.\"},{\"question\":\"Which learning behaviors were associated with better exam performance?\",\"answer\":\"Proactive and critical engagement—such as active trial, error correction, and verification—was strongly associated with significantly better performance. Limited engagement (e.g., abstaining or adopting AI output rote) was associated with worse outcomes.\"}]",1784206440,134,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/learning-behavior-accounts-for-background-related-advantage-in-ai-assisted-education/85817/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in AI-assisted education research?","Question",{"text":75,"@type":76},"Existing AI-for-education studies show inconsistent average effects, while prior work on educational technologies suggests these averages hide heterogeneity across student groups. The study focuses on why some students benefit more than others when using AI for learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the study conducted to examine learning behavior and outcomes?",{"text":80,"@type":76},"The study recruited 318 university students for structured experiments lasting up to 125 minutes. Participants worked in two course contexts (Python or game theory), with students randomly assigned to an experimental group with GPT access or a control group without LLM tool access, including pre- and post-task surveys and an exam without GPT access for all.",{"name":82,"@type":73,"acceptedAnswer":83},"Which learning behaviors were associated with better exam performance?",{"text":84,"@type":76},"Proactive and critical engagement—such as active trial, error correction, and verification—was strongly associated with significantly better performance. Limited engagement (e.g., abstaining or adopting AI output rote) was associated with worse outcomes.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]