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Xiaoqing Xu; Lifang Qiao; Nuo Cheng; Hongxia Liu; Wei Zhao – British Journal of Educational Technology, 2025
The rapid development of generative artificial intelligence (GenAI) has brought opportunities and new challenges to higher education. Students need a high level of self-regulated learning to adapt to this change. However, it is difficult for students to persist in self-regulation without guidance. Metacognitive support has a significant advantage…
Descriptors: Independent Study, Learning Experience, Artificial Intelligence, Technology Uses in Education
Leisi Pei; Morris Siu-Yung Jong; Junjie Shang; Guang Ouyang – British Journal of Educational Technology, 2025
Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video-based learning (VBL), tracking real-time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning…
Descriptors: Diagnostic Tests, Brain, Cognitive Processes, Difficulty Level
Jeongwon Lee; Dongho Kim – British Journal of Educational Technology, 2025
Despite the rapid growth of video-based learning in higher education, students face challenges in sustaining their full attention on video lectures. While gamification has been used to engage students, there have been few attempts to gamify in-video learning. The study aimed to create a gamified instructional video and examine the effects on…
Descriptors: Higher Education, Gamification, Video Technology, Technology Uses in Education
Giulia Cosentino; Jacqueline Anton; Kshitij Sharma; Mirko Gelsomini; Michail Giannakos; Dor Abrahamson – British Journal of Educational Technology, 2025
This study explores the role of generative AI (GenAI) in providing formative feedback in children's digital learning experiences, specifically in the context of mathematics education. Using multimodal data, the research compares AI-generated feedback with feedback from human instructors, focusing on its impact on children's learning outcomes.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Feedback (Response), Mathematics Education
Chia-Jung Li; Gwo-Jen Hwang; Ching-Yi Chang; Hui-Chi Su – British Journal of Educational Technology, 2025
In professional training, developing critical thinking is essential for professionals to analyse problem situations and respond effectively to emergencies. Conventional professional training typically employs multimedia materials combined with progressive prompting (PP) to support trainees in constructing knowledge and solving problems on their…
Descriptors: Artificial Intelligence, Technology Uses in Education, Prompting, Academic Achievement
Jingjing Ma; Qingtang Liu; Shufan Yu; Jindian Liu; Xiaojuan Li; Chunhua Wang – British Journal of Educational Technology, 2025
This research employs the fuzzy-set qualitative comparative analysis (fsQCA) method to investigate the configurations of multiple factors influencing scientific concept learning, including augmented reality (AR) technology, the concept map (CM) strategy and individual differences (eg, prior knowledge, experience and attitudes). A quasi-experiment…
Descriptors: Science Education, Scientific Concepts, Comparative Analysis, Qualitative Research
Michaela Arztmann; Jessica Lizeth Domínguez Alfaro; Lisette Hornstra; Jacqueline Wong; Johan Jeuring; Liesbeth Kester – British Journal of Educational Technology, 2025
A distinct feature of educational games using augmented reality (AR) is that the game is played through physically interacting with the environment, whereas physical interaction is typically rather limited in other digital games. Understanding and performing the interactive game mechanics can be cognitively demanding. Adding pre-training could…
Descriptors: Computer Simulation, Artificial Intelligence, Training, Cognitive Processes
Anna Y. Q. Huang; Cheng-Yan Lin; Sheng-Yi Su; Stephen J. H. Yang – British Journal of Educational Technology, 2025
Programming education often imposes a high cognitive burden on novice programmers, requiring them to master syntax, logic, and problem-solving while simultaneously managing debugging tasks. Prior knowledge is a critical factor influencing programming learning performance. A lack of foundational knowledge limits students' self-regulated learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Coding, Programming

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