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Andrew Kemp; Edward Palmer; Peter Strelan; Helen Thompson – British Journal of Educational Technology, 2024
Many technology acceptance models used in education were originally designed for general technologies and later adopted by education researchers. This study extends Davis' technology acceptance model to specifically evaluate educational technologies in higher education, focusing on virtual classrooms. Prior research informed the construction of…
Descriptors: College Students, Educational Technology, Models, Student Attitudes
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
Weikang Lu; Chenghua Lin – Education and Information Technologies, 2025
Artificial intelligence is increasingly integrated into daily life, and modern educated individuals should have the ability to use AI tools correctly to improve work, study, and life efficiency. In this context, artificial intelligence literacy has been proposed. Due to the lack of consensus on the constructs of artificial intelligence literacy,…
Descriptors: Artificial Intelligence, Digital Literacy, Student Attitudes, College Students
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
Kivanç Bozkus; Özge Canogullari – Education and Information Technologies, 2025
This study investigated the relationships between academic self-discipline, self-control and management, meaningful learning self-awareness, and generative artificial intelligence (GAI) acceptance among 597 teacher candidates at nine Turkish universities. A serial mediation model was proposed, hypothesizing that academic self-discipline influences…
Descriptors: Self Control, Self Management, Self Concept, Computer Attitudes
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
Tasdöndüren, Tuba; Korucu, Agah Tugrul – Journal of Learning and Teaching in Digital Age, 2022
The aim of this study is to examine middle school students' perceptions of information technology self-efficacy and their attitudes towards coding according to various variables and to determine the difference between secondary school students' perceptions of information technology self-efficacy and their attitudes towards coding. The study was…
Descriptors: Student Attitudes, Computer Attitudes, Self Efficacy, Programming
Lihui Sun; Liang Zhou – Education and Information Technologies, 2025
Generative Artificial Intelligence (GenAI) has fundamentally transformed the education landscape, offering unprecedented potential for personalized learning and enhanced teaching methods. This research conducted two sub-studies aimed at exploring the influences and differences in college students' attitudes towards generative artificial…
Descriptors: Artificial Intelligence, Computer Uses in Education, Computer Attitudes, Student Attitudes
Manuela Farinosi; Claudio Melchior – European Journal of Education, 2025
Artificial intelligence (AI) tools have the potential to revolutionise educational practices, but their use raises ethical and organisational concerns for higher education institutions (HEIs). We investigated Italian students' perception and usage of AI tools at the University of Udine using questionnaires (N = 531) with fixed and open-ended items…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Attitudes, Computer Attitudes
Yulu Cui; Hai Zhang – Education and Information Technologies, 2025
With the development of artificial intelligence technology, it has become increasingly difficult to distinguish between Artificial Intelligence Generated Content (AIGC) and non-AIGC. Inaccuracies in identifying AIGC in higher education may lead to academic misconduct and risks, and the credibility of AIGC is also subject to certain doubts. Users…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Chun-Mei Chou; Tsu-Chuan Shen; Tsu-Chi Shen – Education and Information Technologies, 2025
AR-supported instruction has been verified to improve students' problem-solving skills. This study investigated 1041 university students and developed an empirical research model that combined technology acceptance, self-regulation, and AR-supported learning effectiveness with the structural equation model (SEM). At the same time, content analysis…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Adoption (Ideas)
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
Matt Marino – Journal of Research Initiatives, 2024
This article explores the disconnect between student and educator perspectives regarding practical technology usage in higher education. As technology continues to play an increasingly prominent role in the educational landscape, understanding the differing viewpoints of students and educators is crucial for designing impactful technology…
Descriptors: College Students, College Faculty, Teacher Attitudes, Student Attitudes
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

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