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Perceived usefulness and perceived ease of use are tested as predictors of intention to use AI. Correlational and median difference analyses examine relationships among variables, while inferential tests assess the influence of prior technology experience and institutional support. Hierarchical regression evaluates moderation, finding prior experience strengthens the usefulness–intention link and faculty grouping yields perceptual differences.",{"@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":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/acceptance-of-artificial-intelligence-tools-among-undergraduates-an-application-of-the-technology-acceptance-model/582258/",{"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/acceptance-of-artificial-intelligence-tools-among-undergraduates-an-application-of-the-technology-acceptance-model/582258.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study apply the Technology Acceptance Model to AI tools in education?","Question",{"text":112,"@type":113},"It uses TAM constructs perceived usefulness and perceived ease of use to predict students' intention to use AI tools in university teaching and learning contexts.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which factors influence perceived usefulness, perceived ease of use, and intention to use AI?",{"text":117,"@type":113},"Prior experience with technology and institutional support are identified as external variables influencing perceived usefulness, perceived ease of use, and intention to use AI.",{"name":119,"@type":110,"acceptedAnswer":120},"Do students from different academic faculties show differences in AI acceptance?",{"text":121,"@type":113},"Yes. Participants grouped by academic faculties show significant differences in their perceptions of TAM variables, indicating variability across faculties.","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},582258,1792499509,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"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},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","TOJET: The Turkish Online Journal of Educational Technology – January 2026, volume 25 Issue 1  \nAcceptance of Artificial Intelligence Tools Among Undergraduates: An Application of the Technology Acceptance Model  \nEdwin Osmil COREAS-FLORES  \nUniversidad Gerardo Barrios, El Salvador  \nORCID: 0000-0003-4380-5602  \n[osmil@ugb.edu.sv](osmil@ugb.edu.sv)  \nSUMMARY  \nThis study investigated the application of the Technology Acceptance Model (TAM) to assess the acceptance and adoption of Artificial Intelligence tools in educational contexts. The research focused on analyzing the attitudes of university students towards the implementation of AI technologies in teaching and learning processes. Methodology: The study used the TAM theoretical framework, focusing on two main constructs: perceived usefulness and perceived ease of use as predictors of intention to use AI tools in education. Correlational and median difference statistical analyses were applied to examine the relationships between these variables in a sample of students. Key findings: Results revealed significant correlations between perceived usefulness and intention to use AI, as well as between perceived ease of use and behavioural intention. Inferential analysis demonstrates that the external variables prior experience with technology and institutional support influence perceived usefulness, perceived ease of use and intention to use AI tools in university higher education. In addition, hierarchical regression was used to analyze the moderation of external variables in the TAM model, finding that previous experience with technology significantly enhances the relationship between perceived usefulness and intention to use (β = .35, p = .001), increasing the explained variance to 53% in the final model. On the other hand, student participants, grouped into academic faculties, show significant differences in the perception of the TAM variables. Conclusions: The study confirms the applicability of the TAM model in the educational context for AI technologies, suggesting that both perceived benefits and usability and institutional support are critical factors in promoting the successful adoption of these tools in academic settings.  \nKeywords: TAM, Artificial Intelligence, University Higher Education  \nINTRODUCTION  \nThe integration of Artificial Intelligence (AI) in higher education is transforming traditional teaching and learning paradigms (Zawacki-Richter et al., 2019) . However, the adoption of these tools has not been uniform across universities, generating debate about their effectiveness and acceptance.  \nProblematic situation  \nInternationally, the integration of digital technology in education has allowed the democratization of access to information, expanding educational coverage and implementing interactivity through learning environments (Araujo Bedoya et al., 2024), which is redefining the future in terms of learning (Vera & García-Martínez, 2022) . This technological adoption has generated a paradigm shift in the integration of educational technology (Gros et al., 2020), which has undoubtedly led universities to consider its challenges and opportunities (Carrión Salinas & Andrade-Vargas, 2024) .  \nThe implementation of AI tools in higher education varies significantly. Holmes et al. (2019) and Pedreño Muñoz (2022) say that there has been an increase in the adoption of AI in education globally, with countries such as the United States, Spain and China adopting these technologies as intelligent tutoring systems (ITS) and being welcomed by education stakeholders; while, during the 2020-2025 period, generative artificial intelligence has been adopted as a teaching strategy that improves teaching and learning.(Echeverria Quiñonez & Otero Mendoza, 2025) .  \nIn Latin America, the integration of technology in university education is in its early stages, with challenges related to technological infrastructure and teacher training (Holmes et al., 2019; Luckin et al., 2016; P","cbCaimyu70OXsYCM","https://ap.wps.com/l/cbCaimyu70OXsYCM","pdf",342188,17,"English","# Introduction\n## Problematic situation\n## Research questions\n## Objectives","[{\"question\":\"How does the study apply the Technology Acceptance Model to AI tools in education?\",\"answer\":\"It uses TAM constructs perceived usefulness and perceived ease of use to predict students' intention to use AI tools in university teaching and learning contexts.\"},{\"question\":\"Which factors influence perceived usefulness, perceived ease of use, and intention to use AI?\",\"answer\":\"Prior experience with technology and institutional support are identified as external variables influencing perceived usefulness, perceived ease of use, and intention to use AI.\"},{\"question\":\"Do students from different academic faculties show differences in AI acceptance?\",\"answer\":\"Yes. Participants grouped by academic faculties show significant differences in their perceptions of TAM variables, indicating variability across faculties.\"}]","Acceptance of Artificial Intelligence Tools Among Undergraduates: An Application of the Technology Acceptance Model | PDF",1792042908,43]