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Chengliang Wang; Xiaojiao Chen; Zhebing Hu; Sheng Jin; Xiaoqing Gu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Computer Uses in Education
Mehrbakhsh Nilashi; Rabab Ali Abumalloh – Education and Information Technologies, 2025
Immersive technologies strive to enhance users' digital experiences by enabling more interactive, engaging, and realistic virtual environments. Despite the growing popularity and advancements in immersive technologies, achieving widespread user acceptance remains a significant challenge. In addition, previous acceptance models may not fully…
Descriptors: Computer Attitudes, Computer Simulation, Simulated Environment, Physical Environment
Zhu Zhu; Yingying Ren; An ran Shen – Education and Information Technologies, 2025
Current educational trends leverage artificial intelligence (AI) to provide high-quality teaching and enhance students' learning competitiveness. This study aimed to evaluate the acceptance of artificial intelligence generated content (AIGC) for assisted learning and design creation among art and design students. Based on an extended technology…
Descriptors: Artificial Intelligence, Computer Assisted Design, Computer Assisted Instruction, Art Education
Ahmet Volkan Yüzüak; Emrah Higde; Zekiye Merve Öcal; Görkem Avci; Sinan Erten – International Journal of Assessment Tools in Education, 2025
In today's educational landscape, students have access to enriched learning environments through augmented and virtual reality (AR/VR) applications. Effective digital learning depends on identifying the key factors and learner attitudes that influence engagement and task performance. We focused more on preservice teachers' intentions to use AR/VR…
Descriptors: Computer Simulation, Computer Uses in Education, Preservice Teachers, Intention
Mussa Saidi Abubakari; Gamal Abdul Nasir Zakaria; Juraidah Musa – Cogent Education, 2024
Various factors, including technical, organisational, cultural, and individual, can influence how people adopt digital technologies (DT). However, different contexts have produced similar yet distinct results when researchers integrated these various factors into the technology acceptance model (TAM). Two critical factors in the Islamic…
Descriptors: Foreign Countries, Higher Education, Islam, Religious Education
Anna Korchak; Ghadah Al Murshidi; Aleksandra Getman; Noor Raouf; Marwa Arshe; Nawal Al Meheiri; Galina Shulgina; Jamie Costley – Innovations in Education and Teaching International, 2025
This study explores the role of social influence in the adoption strategies of generative artificial intelligence (GenAI) among graduate and undergraduate students. Using the Unified Theory of the Acceptance and Use of Technology (UTAUT) and its key behaviour intention determinant, social influence, the relationship between GenAI popularity among…
Descriptors: Foreign Countries, Undergraduate Students, Graduate Students, Artificial Intelligence
Fairuz Anjum Binte Habib – Education and Information Technologies, 2025
The incorporation of artificial intelligence (AI) into education is becoming more important over time, although faculty viewpoints on this integration are not well recognized. To analyze educators' attitudes towards AI tools in Bangladesh, this research built a modified model that included components from the technology acceptance model (TAM),…
Descriptors: Teacher Attitudes, Intention, Artificial Intelligence, Technology Uses in Education
Linlin Hu; Hao Wang; Yunfei Xin – Education and Information Technologies, 2025
Although Generative Artificial Intelligence (GAI) has demonstrated significant potential in education, there is a lack of research on pre-service teachers' behavioral intentions toward GAI. This study is based on the UTAUT2 model and, for the first time, introduces perceived risk as a key variable to systematically investigate the factors…
Descriptors: Foreign Countries, Preservice Teachers, Computer Attitudes, Technology Integration
Tugce Özbek; Christina Wekerle; Ingo Kollar – Education and Information Technologies, 2024
Pre-service teachers' often suboptimal use of technology in teaching can be explained by low levels of technology acceptance. The present study aims to investigate how technology acceptance can be promoted. Based on the Technology Acceptance Model by Davis (1986), we hypothesized that encouraging pre-service teachers to constructively engage with…
Descriptors: Preservice Teachers, Student Attitudes, Computer Attitudes, Technology Uses in Education
Kamil Çelik; Ahmet Ayaz – Educational Technology Research and Development, 2025
Technological advancements in recent years have accelerated the development of information and communication technologies, introducing numerous innovations. One prominent innovation is the concept of the metaverse, which has gained significant popularity and is increasingly influencing various sectors, including the economy, art, entertainment,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Intention, Computer Science Education
Izida I. Ishmuradova; Alexey A. Chistyakov; Tatyana A. Brodskaya; Nikolay N. Kosarenko; Natalia V. Savchenko; Natalya N. Shindryaeva – Contemporary Educational Technology, 2025
This investigation aimed to ascertain latent profiles of university students predicated on fundamental factors influencing their intentions to acquire knowledge in artificial intelligence (AI). The study scrutinized four dimensions: supportive social norms, facilitating conditions, selfefficacy in AI learning, and perceived utility of AI. Through…
Descriptors: Artificial Intelligence, Technology Uses in Education, College Students, Electronic Learning
Areen Hazzan-Bishara; Ofrit Kol; Shalom Levy – Education and Information Technologies, 2025
This study examines factors influencing teachers' intention to adopt Generative AI technologies in education by extending the Technology Acceptance Model (TAM). The proposed comprehensive model incorporates both external factors (exposure to AI information, information credibility, and institutional support) and internal factors (intrinsic…
Descriptors: Technology Uses in Education, Artificial Intelligence, Teacher Attitudes, Computer Attitudes
Daniel Makini Getuno; Ezra Kiprono Maritim; Fred Nyabuti Keraro – International Journal of Education and Development using Information and Communication Technology, 2025
This study advances an e-learning adoption model by exploring the link between Performance Expectancy (PE) and Behavioural Intention (BI) to adopt e-learning among undergraduate students in Kenya's public universities. Using the Unified Theory of Acceptance and Use of Technology (UTAUT), data were collected from a sample of 388 respondents through…
Descriptors: Foreign Countries, Electronic Learning, Intention, Expectation
Wang, Kai – International Review of Research in Open and Distributed Learning, 2023
This study incorporated the technology acceptance model (TAM) and theory of planned behavior (TPB) to interpret students' perception of MOOCs. This study was based on a survey questionnaire; all 525 respondents were undergraduates in China. A five-point Likert scale was used to collect data in order to measure relationships among the constructs of…
Descriptors: Foreign Countries, Undergraduate Students, MOOCs, Intention
Jie Xu; Yan Li; Rustam Shadiev; Cuixin Li – Education and Information Technologies, 2025
Generative Artificial Intelligence (AI) is steadily gaining prominence in higher education and brings about huge impact on college students' daily life. However, limited studies paid attention to college students' use behavior of generative AI and its influencing factors. The study aimed to explore this issue by adopting an extended Unified Theory…
Descriptors: College Students, Technology Uses in Education, Artificial Intelligence, Intention

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