Emerging Technologies in Learning: A Bibliometric Analysis of Technology Integration and Applications

被引:0
|
作者
Khaddar, Al Mahdi [1 ]
Dehdi, Amine [1 ,2 ]
Alali, Abdelhakim [1 ]
Bakhouyi, Abdellah [2 ,3 ]
Talea, Mohamed [1 ]
机构
[1] Laboratory of Information Processing (LTI), Faculty of Sciences Ben M’Sick, University Hassan II of Casablanca, Casablanca, Morocco
[2] DT2IA Team, M2S2I Laboratory, ENSET, Hassan II University, Casablanca, Morocco
[3] ENSAD Casablanca, Hassan II University of Casablanca, Casablanca, Morocco
关键词
Adversarial machine learning - Federated learning;
D O I
10.3991/ijim.v19i05.51267
中图分类号
学科分类号
摘要
Smart learning, a field marked by rapid evolution and innovation, leverages emerging technologies to address modern educational challenges, transforming teaching methodologies and driving significant progress. This study analyzes the impact of key technologies, including artificial intelligence (AI), the Internet of Things (IoT), big data, and generative AI, between 2010 and 2024 using bibliometric and content analysis methods. Drawing from Scopus and Web of Science (WoS) databases, it highlights how these innovations foster personalized learning environments and dynamic educational content. The findings reveal exponential growth in research on AI and the IoT in education since 2015, with major contributions from Chinese and American researchers. The study profiles influential researchers, leading institutions, and pioneering countries, offering insights into the evolving landscape of smart education. By examining trends and the interplay between technology and educational reform, the paper underscores the importance of data-driven strategies for designing and implementing adaptive learning systems. It also anticipates future challenges and opportunities, proposing a framework to guide ethical integration of these technologies into education, ensuring they enhance global learning outcomes in an increasingly digital world. © 2025 by the authors of this article.
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页码:60 / 78
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