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Megan J. Hennessey; Erica Haglund; Audrey Ross; Linda Brent – Innovative Higher Education, 2025
This exploratory study considered students' perceptions of their learning experiences at a U.S. military chaplain college across multiple cohorts over five years to discover possible connections between student learning and the use of immersive learning technologies. Data sources included end-of-course survey results from 490 professional…
Descriptors: Student Attitudes, Technology Uses in Education, Military Service, Military Personnel
Said A. Salloum; Khaled Mohammad Alomari; Aseel M. Alfaisal; Rose A. Aljanada; Azza Basiouni – Smart Learning Environments, 2025
The integration of artificial intelligence in educational environments has the potential to revolutionize teaching and learning by enabling real-time analysis of students' emotions, which are crucial determinants of engagement, motivation, and learning outcomes. However, accurately detecting and responding to these emotions remains a significant…
Descriptors: Artificial Intelligence, Emotional Response, Psychological Patterns, Individualized Instruction
María García de Blanes Sebastián; José Ramón Sarmiento Guede; Alberto Azuara Grande; Antonio Ferrao Filipe – Education and Information Technologies, 2025
During COVID-19 pandemic, Mobile learning (M-learning) was implemented in many universities to continue teaching. Consequently, its use has increased drastically, facing new challenges and benefits. The main aim of this research is to adopt the Unified Theory of Acceptance and Use of Extended Technology (UTAUT-2) to determine the intention of…
Descriptors: Telecommunications, Handheld Devices, Educational Technology, Technology Uses in Education
Mohammed A. E. Suliman; Wenlan Zhang; Rehab A. I. Suluman; Kamal Abubker Abrahim Sleiman – Education and Information Technologies, 2025
This study contributes to the knowledge about mobile learning among medical students in the context of developing countries. This research used the Technology Acceptance Model (TAM) to study the preconditions for m-learning among medical students. A twenty-item self-reported survey was used to gather data from 387 medical students, and structural…
Descriptors: Medical Students, Student Attitudes, Information Technology, Technology Integration
Angelika Johansson Cristvall; Margaretha Larsson; Johanna Tell; Lisa Skär – Journal of School Nursing, 2025
School health services (SHSs) and school nurses play a crucial role in identifying and supporting students with mental illness. The integration of information and communication technology (ICT) can facilitate interorganizational collaboration in this context. Due to the limited research in this area, a scoping review was conducted to explore SHSs'…
Descriptors: School Health Services, Information Technology, Agency Cooperation, Mental Disorders
Feng Feng; Wenxia Yang – Education and Information Technologies, 2025
The emergence of digital game types has opened up significant opportunities to facilitate language acquisition through feedback provision, attracting diverse research strands to explore their potential as arenas for technology-assisted language learning. This study investigates the effects of learner-, peer-, and collaborative-regulated feedback…
Descriptors: Foreign Countries, Computer Games, Game Based Learning, Feedback (Response)
Antía González Ben; Jess Mullen – Action, Criticism, and Theory for Music Education, 2025
Employing Stephen Ball's notion of network governance, this study examines the relationships between private companies, non-profit organizations, and public institutions involved with music education in the United States (US) and Canada during the COVID-19 pandemic. To identify which public and private actors had a hand in shaping music education…
Descriptors: Foreign Countries, Music Education, COVID-19, Pandemics
Miswandi Tendrita; Ully Hidayati – Journal of Biological Education Indonesia (Jurnal Pendidikan Biologi Indonesia), 2025
Collaboration skills are essential skills that students must have in the era of higher education, where they must work together to complete complex tasks. This study intends to examine the effectiveness of project-based learning (PjBL) supported by artificial intelligence-driven mind mapping in enhancing the collaboration skills of pre-service…
Descriptors: Cognitive Mapping, Artificial Intelligence, Technology Uses in Education, Student Projects
Yaki A. Akawo; Mustapha M. Tajordeen; Kabir S. Fatima; Hussain B. Mufida – Journal of Science Learning, 2025
The study examined Nigeria Science and Mathematics Teachers' intention towards adopting the CL4STEM technology-based instructional (OER) modules to enhance higher-order thinking with Inclusion and Equity (HOTIE). The study adopted a descriptive correlational design. The population of the study was all mathematics and science teachers in some…
Descriptors: Science Teachers, Mathematics Teachers, Teacher Attitudes, Intention
Marissa R. Bamberger; Olha Ketsman; Jui-Ling Chiang; Akosua B. Poku; Kara Kennedy; Mary Sanderson; Todd D. Reeves – Journal of Educational Technology Systems, 2025
This exploratory study describes the distribution of pre-service teacher (PST) technology use for formal or informal professional learning. Additionally, it describes PSTs' self-efficacy for technology use and associated factors. Laptops and smartphones were among the most commonly used devices. Technology use for professional learning was common…
Descriptors: Educational Technology, Technology Uses in Education, Preservice Teacher Education, Preservice Teachers
Rebekah Davis; Florence Martin; Min Zhuang; Duncan Culbreth; Cigdem Meral; WeiChao Wang – Journal of Educational Technology Systems, 2025
Monitoring applications (MAs) software provides filtering and monitoring for technology management. In the fall of 2020, a southeastern state encouraged public and charter K-12 schools to use MAs by providing funding for their purchase. This was intended to protect students from online threats and self-harm during the COVID-19 pandemic, which…
Descriptors: Technology Uses in Education, COVID-19, Pandemics, Bullying
The Effects of Three Different Approaches to Human-AI Collaboration on Online Collaborative Learning
Juliana Fosua Gyasi; Lanqin Zheng; Stephen Frank Love; Francis Ohene Boateng – Educational Technology & Society, 2025
Online collaborative learning has the potential to help learners of all cultures and languages in the artificial intelligence (AI) age. However, studies on the use of human-AI collaboration to promote online collaborative learning are lacking. This study attempts to fill this gap by examining the effects of three approaches to human-AI…
Descriptors: Cooperative Learning, Online Courses, Artificial Intelligence, Program Effectiveness
Antony Musasa; Jameson Goto; Geoffrey Lautenbach – Contemporary Educational Technology, 2025
Educators must effectively integrate technology into their teaching practices in today's technology-driven world. This study investigated factors influencing technology integration into teaching among mathematics educators in Gauteng secondary schools in South Africa. The unified theory of acceptance and use of technology, extended by adding the…
Descriptors: Educational Technology, Technology Integration, Mathematics Education, Mathematics Teachers
Le Thai Bao Thien Trung; Ta Thanh Trung; Tang Minh Dung – Journal of Pedagogical Research, 2025
In the context of the widespread integration of artificial intelligence (AI) technology into education, this study aims to understand the factors influencing Vietnamese mathematics teachers' adoption of ChatGPT in secondary school teaching. Based on the Unified Theory of Acceptance and Use of Technology model, the study uses a quantitative…
Descriptors: Foreign Countries, Artificial Intelligence, Technology Uses in Education, Technology Integration
Ce Song – European Journal of Education, 2025
This study examines the role of AI-powered learning tools in influencing cognitive load, well-being and academic success among music education students, with a focus on technology acceptance as a key factor. Data were collected through a random sampling of 454 Chinese music students (192 males, 262 females) aged 18-24, with varying levels of…
Descriptors: Artificial Intelligence, Technology Uses in Education, Influence of Technology, Music Education

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