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Showing 16 to 30 of 546 results Save | Export
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Zhao Li; Jirawan Deeprasert; Songyu Jiang – African Educational Research Journal, 2024
This study employs Structural Equation Modeling (SEM) and the Technology Acceptance Model (TAM) framework to explore the factors influencing Massive Open Online Courses (MOOCs) usage among college students in Southwest China. Using probability sampling, data were collected from 602 participants through an online survey distributed over a period of…
Descriptors: Foreign Countries, MOOCs, Usability, College Students
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Mirjana Maricic; Branko Andic; Soeharto Soeharto; Filiz Mumcu; Stanko Cvjeticanin; Zsolt Lavicza – Education and Information Technologies, 2025
According to the theoretical frameworks and teaching practice, the constructs of the Technology acceptance model - TAM and the Cognitive load theory - CLT are in a close cause-and-effect relationship, and gaining insights into this issue is essential for educators. Our study aimed to examine continuous teaching intention (CTI) with emerging…
Descriptors: Teacher Attitudes, Intention, Technology Uses in Education, Elementary School Teachers
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Thinley Wangdi; Karma Sonam Rigdel – Journal of Education for Teaching: International Research and Pedagogy, 2025
This qualitative study explored the factors that influence teachers' behavioural intention (BI) to use ChatGPT for teaching. The data was collected from 214 Bhutanese teachers using open-ended questions and interviews. The thematic analysis revealed four key factors that are likely to influence teachers' BI to use ChatGPT in the context. These…
Descriptors: Teacher Attitudes, Intention, Artificial Intelligence, Computer Software
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Chengming Zhang; Min Hu; Weidong Wu; Farrukh Kamran; Xining Wang – Education and Information Technologies, 2025
Artificial intelligence (AI) integration in education has grown significantly recently. However, the potential risks of AI have led to educators being wary of implementing AI systems. To discover whether AI systems can be effective in the classroom in the future, it is critical to understand how risk factors (e.g., perceived safety risks,…
Descriptors: Foreign Countries, Artificial Intelligence, Trust (Psychology), Preservice Teachers
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Ana M. Gallardo-Guerrero; María J. Maciá-Andreu; Noelia González-Gálvez; Raquel Vaquero-Cristóbal; Marta García-Tascón – Education and Information Technologies, 2025
The main objectives of this research were to analyze the impact of the use of augmented reality (AR) for analyzing the safety of sport equipment, on the motivational climate, behavior, and intention to use, and to validate a theoretical model for predicting continuance intention to use AR among students. The sample consisted of 254 university…
Descriptors: Simulated Environment, Safety, Athletics, Equipment
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Balázs Fajt; Emese Schiller – Journal of Academic Ethics, 2025
This mixed-methods study examines the integration of ChatGPT within academic discourse, focusing on its role in education. ChatGPT, an AI chatbot using the GPT model, offers significant benefits such as enhanced plagiarism detection and improved accuracy in academic work. However, it also presents ethical challenges related to academic integrity.…
Descriptors: Artificial Intelligence, Technology Uses in Education, College Students, Student Attitudes
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Chenxi Liu; Ana-Paula Correia; Young Min Kim – Educational Technology Research and Development, 2025
Mobile learning can positively impact learning in different aspects, but the retention rate of mobile learning applications could be better. Based on the Technology Acceptance Model and the updated DeLone and McLean Information System Success Model, this study develops a novel model to examine the determinants of learners' acceptance of mobile…
Descriptors: Telecommunications, Handheld Devices, Technology Uses in Education, Learning Activities
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Tony Robinson – Journal of Educational Technology, 2025
Generative artificial intelligence (AI) is increasingly transforming higher education by enhancing teaching methodologies, automating administrative tasks, and supporting research initiatives. Faculty adoption of generative AI is crucial for maximizing its potential benefits; however, its acceptance remains inconsistent due to factors such as…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Technology Integration
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Saeed Alzahrani; Anish Kumar Bhunia – Educational Process: International Journal, 2025
Background/purpose: The present study utilizes an integrated theoretical framework that integrates the Theory of Planned Behaviour, Technology Acceptance Model, and Value-Based Adoption Model to explore the effects of Digital Literacy (DL) on the behavioral intention of the Saudi Generation Z students toward adopting Fintech (FAI). It emphasizes…
Descriptors: Foreign Countries, Technology Integration, Educational Technology, Technological Literacy
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Yi Chen; Honghe Gao – SAGE Open, 2025
Against the backdrop of rapid advancements in information technology, Information and Communication Technology (ICT) has been widely applied in art education. Whether teachers accept and use these tools largely depends on their digital literacy. This study explores how teachers' digital literacy influences their ICT integration intention based on…
Descriptors: Technological Literacy, Technology Uses in Education, Art Education, Technology Integration
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Ravi Sankar Pasupuleti; Deevena Charitha Jangam; Anitha Bhimavarapu; Venkata Reddy Gunnam; Venkata Ramana Sikhakolli; Deepthi Thiyyagura – Electronic Journal of e-Learning, 2025
This research explores adoption of the Deepseek, an artificial intelligence (AI) platform among higher education students in India by integrating the Technology Acceptance Model (TAM) with learning motivation factors. Given the rapid rise of AI-based platforms in educational sector, understanding their adoption is not only timely but also…
Descriptors: Foreign Countries, Artificial Intelligence, Technology Uses in Education, College Students
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Ben Siu; John White – British Educational Research Journal, 2025
This study employs the Technology Readiness and Acceptance Model to uncover the factors influencing university students' intentions to adopt generative artificial intelligence (AI) technologies in higher education. With the rapid integration of Generative AI into academic contexts, understanding what drives students' intention to use these tools…
Descriptors: Technology Uses in Education, Artificial Intelligence, College Students, Student Attitudes
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Caleb Or – International Journal of Technology in Education and Science, 2024
The Technology Acceptance Model (TAM), proposed by Fred Davis in 1986, is a foundational framework for understanding technology adoption, emphasizing Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) as key determinants of Intention to Use (ITU). While Attitude Toward Using (ATU) was initially central to TAM, it was later omitted in…
Descriptors: Attitudes, Technological Literacy, Usability, Value Judgment
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Pengfei Yang; Shaowen Qian – SAGE Open, 2025
E-learning has revolutionized the educational landscape, changing how knowledge is imparted to students and enhancing the learning process. Despite the growing popularity of e-learning worldwide, a lingering question remains regarding the behavioral intentions of Physical Education students toward its use. This study endeavors to address this…
Descriptors: Foreign Countries, Physical Education, Intention, Electronic Learning
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Chun-Hua Hsiao; Kai-Yu Tang – Education and Information Technologies, 2025
The rapid development of generative AI (GenAI), such as ChatGPT, has created both opportunities and challenges for its use in education. Some educators have expressed concern that such rapid report generation from AI may encourage cheating or hinder the development of critical thinking skills in students. Moving beyond mere acceptance, we propose…
Descriptors: Technology Uses in Education, Artificial Intelligence, Higher Education, Ethics
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