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A Retrospective Analysis of Artificial Intelligence in Education (AIEd) Studies: Perspectives, Learning Theories, Challenges, and Emerging Opportunities
Linnaeus University, Faculty of Arts and Humanities, Department of Cultural Sciences. Linnaeus University, Linnaeus Knowledge Environments, Digital Transformations.ORCID iD: 0000-0002-0025-118X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-4144-6012
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-6937-345X
2024 (English)In: Radical Solutions for Artificial Intelligence and Digital Transformation in Education: Utilising Disruptive Technology for a Better Society / [ed] Daniel Burgos, John Willian Branch, Ahmed Tlili, Ronghuai Huang, Mohamed Jemni, Christian M. Stracke, Colin de la Higuera, Chee-Kit Looi, Khalid Berrada, Springer Nature, 2024, p. 127-141Chapter in book (Refereed)
Abstract [en]

Recent advances in Information and Communication Technologies (ICT), especially AI-powered tools, are driving innovation and productivity across academic and industrial sectors. These technologies are also transforming education by enabling personalized learning, redefining teaching methods, and enhancing educational outcomes. Given the transformative potential of Artificial Intelligence in Education (AIEd), it is essential to examine its benefits and implications for the future of education. AI tools offer substantial benefits to various educational stakeholders by enhancing cognitive engagement, improving teaching assessments, and fostering digital literacy. Additionally, they support policy development, address ethical considerations, and increase educational efficiency and accessibility. However, despite AI’s potential in education, the rapid pace of technological change can create uncertainty and overwhelm, even among experts. As AIEd advances, it is crucial to develop strategies that help educators and institutions stay updated on new developments and integrate them effectively into educational settings. This study explores the historical development of AIEd and identifies current research trends through a dual-method approach. First, a bibliometric analysis using VOSViewer uncovers patterns in AIEd research over the past 50 years. Second, text mining techniques using term frequency analysis and topic modeling—examine over 2,204 abstracts from Scopus, covering publications from 1970 to the present. By combining these methods, the study highlights key AIEd research areas that impact stakeholders and influence future educational practices. Understanding the evolution of AIEd and current trends is essential for anticipating its future trajectory, preparing us to address emerging challenges and opportunities in education. The findings trace the progression of AIEd research, revealing shifts in focus, underlying learning theories, and application paradigms. This comprehensive overview helps identify research gaps and emerging themes, supporting the development of strategies to leverage AIEd’s potential in transforming education and meeting the complex, evolving needs of twenty-first century stakeholders.

Place, publisher, year, edition, pages
Springer Nature, 2024. p. 127-141
Series
Lecture Notes in Educational Technology, ISSN 2196-4963, E-ISSN 2196-4971
Keywords [en]
Artificial intelligence in education, Learning theories, Text mining, VOSViewer, AI literacy
National Category
Educational Sciences Computer and Information Sciences
Research subject
Pedagogics and Educational Sciences; Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-135837DOI: 10.1007/978-981-97-8638-1_9Scopus ID: 2-s2.0-85213968653ISBN: 9789819786374 (print)ISBN: 9789819786381 (electronic)OAI: oai:DiVA.org:lnu-135837DiVA, id: diva2:1934392
Available from: 2025-02-04 Created: 2025-02-04 Last updated: 2025-04-22Bibliographically approved

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fulltext(397 kB)35 downloads
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Mohammed, Ahmed TaiyeVelander, JohannaMilrad, Marcelo

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