[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-590287-105":59,"doc-detail-590287-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","exploring-prospective-teachers-intentions-for-artificial-intelligence-integration-in-education-the-role-of-motivation","Exploring Prospective Teachers’ Intentions for Artificial Intelligence Integration in Education - The Role of Motivation","","Investigates what motivates prospective teachers to intend integrating artificial intelligence in education. The study addresses gaps left by technology acceptance approaches by applying expectancy-value theory and modeling expectancy, attainment, utility, intrinsic value, and cost as predictors of behavioral intention. Data from 454 prospective teachers were analyzed with structural equation modeling using established intention and AI-use-motive measures. Utility value emerged as the strongest predictor, with cost showing a negative effect.",{"@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/exploring-prospective-teachers-intentions-for-artificial-intelligence-integration-in-education-the-role-of-motivation/590287/",{"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/exploring-prospective-teachers-intentions-for-artificial-intelligence-integration-in-education-the-role-of-motivation/590287.png","ImageObject",300,407,{"name":92,"@type":93},"Pentious","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What theoretical framework explains prospective teachers’ intentions toward AI integration?","Question",{"text":112,"@type":113},"The study uses expectancy-value theory, linking intention to expectancy, attainment, utility, intrinsic value, and perceived cost.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which motivational factor most strongly predicts intention to integrate AI?",{"text":117,"@type":113},"Utility value is the strongest positive predictor, indicating that perceived practical benefits drive adoption intentions.",{"name":119,"@type":110,"acceptedAnswer":120},"How do perceived costs and other values affect behavioral intention?",{"text":121,"@type":113},"Perceived cost negatively predicts intention, while intrinsic value, attainment, and expectancy also show positive—though smaller—relationships in the model.","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},590287,1792161297,{"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":19,"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,"preview_page_count":4},1374404730887,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","AERA Open  \nJanuary-December 2025, Vol. 11, No. 1, pp. 1–18 DOI: 10. 1177/23328584251403990  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2025. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero)  \nExploring Prospective Teachers’ Intentions for Artificial Intelligence Integration in Education: The Role of Motivation  \nEyüp Yurt  \nBursa Uludağ University  \nİsmail Kaşarcı  \nEskişehir Osmangazi University  \nUnderstanding motivational drivers behind prospective teachers’artificial intelligence (AI) integration intentions is critical. While prior models such as the technology acceptance model overlook motivational dynamics, this study used expectancyvalue theory to assess how expectancy, attainment, utility, intrinsic value, and cost shape intentions. Data from 454 prospective teachers were analyzed via structural equation modeling using the Behavioral Intention Scale and the Questionnaire of Artificial Intelligence Use Motives. Utility value (β = .29, p \u003C .001) was the strongest predictor, followed by cost (β = −.27), intrinsic value (β = .25), attainment (β = .21), and expectancy (β = .10). The model explained 62% of variance in behavioral intention. Control variables, including gender, class level, and AI usage frequency, significantly influenced intentions. Findings suggested that teacher education programs should enhance AI’s perceived utility, address implementation costs, and strengthen expectancy through training to foster adoption. Emphasizing AI’s practical relevance within supportive environments can bridge its potential with classroom integration.  \nKeywords: artificial intelligence, teacher education/development, higher education, psychology, motivation, structural equation modeling, factor analysis, artificial intelligence in education, expectancy-value theory, prospective teachers, technology adoption  \nIntroduction  \nArtificial intelligence (AI), characterized by the ability of machines to perform tasks requiring human-like intelligence, holds transformative potential for education. Its capacity to enable personalized learning, adapt to individual student needs, and provide data-driven insights for educators underscores its growing adoption in educational settings (Balcı, 2024; Lampou, 2023; Xu & Ouyang, 2022) . However, it is essential to acknowledge ongoing scholarly debates regarding AI’s effectiveness in achieving these outcomes. Critics raise concerns about the empirical rigor and reliability of existing evidence, noting that many studies lack rigorous experimental designs, use small sample sizes, and suffer from methodologic transparency issues (Chen, 2025; Şanlı, 2025). Beyond methodologic limitations, questions persist about whether AI genuinely enables individualized learning or merely creates the illusion of personalization through superficial adaptations that fail to capture critical dimensions of learning, such as creativity and critical thinking (Konstantinidis, 2025) . Furthermore, algorithmic bias and equity concerns underscore how AI  \nsystems may perpetuate historical inequalities embedded in educational data, resulting in discriminatory outcomes based on gender, race, or socioeconomic status (Al-Zahrani, 2024; Mokoena & Seeletse, 2025) . Despite these debates, the field’s growing investment in AI—reflected in policy initiatives and institutional adoption—makes it imperative to understand what drives educators toward or away from AI integration. Regardless of definitive evidence on AI’s effectiveness, implementing these technologies fundamentally depends on educators’ willingness to adopt them, underscoring the critical importance of examining the motivational drivers behind prospective teachers’ behavioral intentions toward AI use.  \nTo better understand these motivational drivers, a systematic review of the literature revealed that existing studies predominantly employ ","cbCaifcN2ycSL0DX","https://ap.wps.com/l/cbCaifcN2ycSL0DX","pdf",563600,18,"English","# Introduction\n## Research approach and motivational framework\n## Motivational drivers and study model\n## Findings and implications for teacher education","[{\"question\":\"What theoretical framework explains prospective teachers’ intentions toward AI integration?\",\"answer\":\"The study uses expectancy-value theory, linking intention to expectancy, attainment, utility, intrinsic value, and perceived cost.\"},{\"question\":\"Which motivational factor most strongly predicts intention to integrate AI?\",\"answer\":\"Utility value is the strongest positive predictor, indicating that perceived practical benefits drive adoption intentions.\"},{\"question\":\"How do perceived costs and other values affect behavioral intention?\",\"answer\":\"Perceived cost negatively predicts intention, while intrinsic value, attainment, and expectancy also show positive—though smaller—relationships in the model.\"}]","Exploring Prospective Teachers’ Intentions for Artificial Intelligence Integration in Education - The Role of Motivation | PDF",1792069252,45]