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Latent profile analysis (LPA) is applied to a sample of 391 students from multiple disciplines. Five subgroups emerge—Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics—with different learning goals. Results show tailored educational strategies and inform curriculum and policy for AI education in higher education.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/latent-profiles-of-ai-learning-conditions-among-university-students-implications-for-educational-intentions/600456/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/latent-profiles-of-ai-learning-conditions-among-university-students-implications-for-educational-intentions/600456.png","ImageObject",300,407,{"name":92,"@type":93},"OmBimo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What factors define the students’ latent profiles for learning AI?","Question",{"text":112,"@type":113},"The study uses four dimensions: supportive social norms, facilitating conditions, AI self-efficacy, and perceived utility of AI.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many student subgroups were identified, and what are they?",{"text":117,"@type":113},"Latent profile analysis revealed five subgroups: Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the findings imply for AI education in higher education?",{"text":121,"@type":113},"The results suggest learners require distinct educational strategies and interventions, and they support curriculum and policy development to address different attitudes and preparedness to learn AI.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},600456,1792161072,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},962090893581,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Contemporary Educational Technology, 2025, 17(2), ep565  \n ISSN: 1309-517X (Online)   \n OPEN ACCESS  Research Article  \nLatent profiles ofAI learning conditions among university students: Implications for educational intentions  \nIzida I. Ishmuradova 1*  \n 0000-0002-8191-1644  \nAlexey A. Chistyakov 2  \n 0000-0003-4266-2515  \nTatyana A. Brodskaya 3  \n 0009-0006-2525-7699  \nNikolay N. Kosarenko 4  \n 0000-0002-5061-5551  \nNatalia V. Savchenko 5  \n 0000-0001-8587-9488  \nNatalya N. Shindryaeva 6  \n 0000-0001-6560-2756  \n1 Kazan (Volga region) Federal University, Kazan, RUSSIA  \n2 Peoples’ Friendship University of Russia, Moscow, RUSSIA  \n3 Almetyevsk State Technological University “Petroleum High School”, Almetyevsk, RUSSIA  \n4 Plekhanov Russian University of Economics, Moscow, RUSSIA  \n5 Financial University under the Government of the Russian Federation, Moscow, RUSSIA  \n6 Sechenov First Moscow State Medical University, Moscow, RUSSIA  \n* Corresponding author: [izida-89@mail. ru](izida-89@mail. ru)  \nCitation: Ishmuradova, I. I., Chistyakov, A. A., Brodskaya, T. A., Kosarenko, N. N., Savchenko, N. V., & Shindryaeva, N. N.  \n(2025) . Latent profiles of AI learning conditions among university students: Implications for educational intentions.  \nContemporary Educational Technology, 17(2), ep565. [https://doi.org/10.30935/cedtech/15907](https://doi.org/10.30935/cedtech/15907)  \nARTICLE INFO ABSTRACT  \nReceived: 29 Sep 2024 This investigation aimed to ascertain latent profiles of university students predicated on Accepted: 18 Jan 2025 fundamental factors influencing their intentions to acquire knowledge in artificial intelligence  \n(AI). The study scrutinized four dimensions: supportive social norms, facilitating conditions, selfefficacy in AI learning, and perceived utility of AI. Through the utilization of latent profile analysis (LPA), the investigation endeavored to unveil distinct subgroups of students delineated by unique amalgamations of these factors. The study was carried outwith a cohort of 391 university students from diverse academic disciplines. LPA disclosed five unique subgroups of students: Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics. These categories showed somewhat different goals to learn AI; Enthusiastic Advocates showed the highest intention while Disengaged Critics showed the lowest. The findings enhance the growing corpus of research on AI education in higher education by providing a sophisticated knowledge of the variation among university students about their attitudes and preparedness to learn AI. Subgroups of students show that learners need unique educational strategies and interventions to meet their diverse needs and attitudes. AI is changing many fields, therefore college students must learn about it and prepare for it. The findings advance AI education research and impact curriculum and policy.  \nKeywords: artificial intelligence, higher education, latent profile analysis, situated expectancyvalue theory, intention to learn AI  \nINTRODUCTION  \nArtificial intelligence (AI) has become an important technology that is transforming various aspects of our lives, including education (Sharma, 2023) . With the advancement and proliferation of AI in different fields, it has become critical for university students to acquire AI literacy and skills in order to succeed in an AI-driven future (Southworth et al., 2023) . The rapid development of AI applications, especially generative AI tools such as ChatGPT, has led to intense debates in the education community about their potential benefits and challenges in supporting learning processes (Lavidas et al., 2024; Šumak et al., 2024) .  \nAI integration in higher education presents both opportunities and challenges. While AI technologies offer promising advantages in enhancing learning experiences and academic performance, concerns about appropriate application and possible misuse remain. Recent research h","cbCaidOLg9KfBBha","https://ap.wps.com/l/cbCaidOLg9KfBBha","pdf",336066,18,"English","# Article Info\n## Received and Accepted Dates\n# Abstract\n## Four Determinants and LPA Method\n## Subgroups and Key Differences\n# Introduction\n## AI Literacy and Educational Context\n## Expectancy-Value Theoretical Framework\n## Prior Research on AI Adoption","[{\"question\":\"What factors define the students’ latent profiles for learning AI?\",\"answer\":\"The study uses four dimensions: supportive social norms, facilitating conditions, AI self-efficacy, and perceived utility of AI.\"},{\"question\":\"How many student subgroups were identified, and what are they?\",\"answer\":\"Latent profile analysis revealed five subgroups: Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics.\"},{\"question\":\"What do the findings imply for AI education in higher education?\",\"answer\":\"The results suggest learners require distinct educational strategies and interventions, and they support curriculum and policy development to address different attitudes and preparedness to learn AI.\"}]","Latent Profiles of AI Learning Conditions among University Students - Implications for Educational Intentions | PDF",1792090728,45]