Aug 2026· e-Jurnal Penyelidikan dan Inovasi· Vol 13, pp. 110-124· 0 citations· 35 references
TL;DR
Insightful insights are provided into publication trends, contributors, research themes, and emerging AI technologies in adaptive learning, which could assist researchers, educators, policymakers, and educational technology developers in improving intelligent adaptive learning systems.
Abstract
Artificial intelligence (AI) has emerged as a transformative technology in education, particularly adaptive learning, which supports personalized and learner-centered experiences. Despite the rapid growth of research on AI for adaptive learning, a comprehensive understanding of its publication performance, thematic structure, and technological evolution remains limited. To overcome this gap, the study aims to systematically map and evaluate the development of research on AI for adaptive learning in education using bibliometric methods. A bibliometric analysis was conducted using data from the Scopus database covering the period from 2015 to 2026. The study analyzed 2,354 publications through two complementary methods: performance analysis and science mapping. Performance analysis was used to evaluate publication growth, influential countries, sources, and highly cited documents. Science mapping techniques, including keyword co-occurrence analysis, were used to identify major research themes and the emerging AI technologies landscape. The findings show that research output has significantly risen after 2023 and recorded the highest number of publications in 2025. India emerged as the most productive country, while the United States had the highest citation impact. Citation analysis highlights adaptive and personalized learning systems, intelligent educational systems, and generative AI applications as key intellectual foundations of the field. Science mapping further revealed that machine learning, intelligent tutoring systems, generative AI, learning analytics, and large language models are central AI technological themes in adaptive learning research. These recent trends indicate a growing shift towards conversational AI, generative AI, and personalized intelligent learning environments. In conclusion, this study provides insights into publication trends, contributors, research themes, and emerging AI technologies in adaptive learning, which could assist researchers, educators, policymakers, and educational technology developers in improving intelligent adaptive learning systems.
Artificial intelligence (AI) has increasingly gained attention in primary education due to its potential to support personalized learning, adaptive assessment, and instructional decision-making. Despite the rapid growth of publications, research in this field remains fragmented, with limited understanding of its intellectual structure and thematic evolution. This study aims to systematically map the development, research trends, and thematic trajectories of AI research in primary education through a bibliometric approach. A total of 547 peer-reviewed journal articles indexed in the Scopus database from 2018 to 2025 were analyzed using performance analysis and science mapping techniques supported by VOSviewer. The findings reveal a significant increase in publication output, indicating the consolidation of AI in primary education as a distinct research domain. Keyword co-occurrence and cluster analysis identify three major thematic clusters: (1) educational technology and teacher capacity building, (2) AI-driven instructional innovation and immersive learning, and (3) humanistic and developmental perspectives in primary education. Thematic evolution analysis demonstrates a clear shift from general digital technology integration (2018–2020), to institutional adaptation and teacher readiness (2021–2022), and finally toward an AI-centered and application-oriented research focus emphasizing personalized learning and assessment (2023–2025). However, ethical considerations, child-centered impacts, and long-term developmental outcomes remain underrepresented. This study provides a comprehensive overview of the intellectual landscape of AI research in primary education and offers strategic insights for future research agendas, emphasizing the need for ethically grounded, pedagogically informed, and developmentally appropriate AI implementation.
A bibliometric analysis of recent literature on the integration of AI into active b-learning methodologies in the university context suggests that the convergence between AI and active learning represents a promising path for pedagogical innovation in Higher Education.
Sergio Sargo Lopes, M. Lousã, Jorge Azevedo Simões et al.· Revista EDaPECI· 0 citations
The development of artificial intelligence (AI) has significantly impacted the learning process in higher education. This study aims to analyze the use of artificial intelligence to support the learning process in higher education, including the forms of use, benefits, and challenges it poses. The study used a qualitative descriptive approach with a literature review method. Data were obtained from various scientific journal articles, books, research reports, and institutional documents relevant to the use of AI in higher education. Data analysis was conducted using content analysis by identifying, classifying, comparing, and synthesizing various previous research findings. The results show that AI can be utilized as a learning assistant, a supporter of personalized learning, a developer of teaching materials, a provider of feedback, an assessment supporter, and a tool to enhance student self-directed learning and creativity. The use of AI can also help lecturers increase efficiency in planning and implementing learning. However, the use of AI presents challenges such as information inaccuracy, technology dependency, violations of academic integrity, data privacy, bias, and the potential for a decline in students' critical thinking skills. Therefore, the use of AI in higher education must be carried out ethically, critically, responsibly, and human-centeredly through strengthening AI literacy, improving lecturer competency, institutional policies, and adjusting assessment systems. AI should be positioned as a tool to enhance the learning process, not as a substitute for lecturers or students' thinking processes.
Heppy Sapulete, Lia Khalisa, Afifah Qurrota A'yun et al.· International Journal of Edu...· 0 citations
A comprehensive bibliometric analysis of research at the intersection of AI-focused TPD and classroom pedagogy identifies three dominant research clusters, which highlight a growing shift from technical adoption toward research exploring teachers’ readiness, ethical awareness, and pedagogical innovation when integrating AI.
Bilal Alhawamdeh, Mohd Fadzil Abdul Hanid, N. D. Abd Halim et al.· International journal of tec...· 0 citations
The educational landscape has rapidly shifted toward technology-enhanced learning, where artificial intelligence (AI) and gamification play increasingly important roles in improving engagement and learning outcomes. This study explores the knowledge structure of AI in educational gamification by examining recent pedagogical advancements (student engagement, motivation and academic achievement), technological innovations (machine learning, ChatGPT and augmented reality) and methodological developments in AI-supported gamified learning. It also identifies future research directions at the educational, technological and implementation levels to guide the development of more adaptive, personalized and effective learning environments.
Using a bibliometric approach, a total of 248 documents were collected from the Web of Science (WoS) database and analyzed with VOSviewer software. Science mapping analysis was performed to uncover emerging themes using bibliographic coupling and future trends using co-word analysis.
Four themes were found in bibliographic coupling associated with AI-driven gamified learning, while co-word analysis produced six clusters associated with transforming education through AI gamification. The findings highlight the crucial role of AI technologies and gamification in education to enhance engagement, personalization and learning outcomes.
The unique contribution of this study is its systematic investigation of the knowledge structure of AI and gamification, offering a foundation for future research and practice. This study also provides a comprehensive understanding of how these technologies can be effectively integrated to transform educational experiences.
Muhammad Ashraf Fauzi, Norhana Mohd Aripin, N. Alimin· Asian Education and Developm...· 0 citations
Artificial intelligence (AI) has emerged as a transformative force in higher education, especially with ChatGPT. Despite growing interest, there is a scarcity of bibliometric studies that systematically map the knowledge domain. Technological innovations present challenges and opportunities, but understanding does not keep pace with innovations. Higher education is at the center of the debate about AI impacts and necessary responses for effective integration. There are no studies that map the intellectual structure through co-citation nor emerging frontiers via bibliographic coupling about AI in higher education. Objective: to map the emergence, evolution and knowledge frontiers about artificial intelligence in higher education through robust bibliometric analyses, identifying consolidated theoretical clusters, emerging trends and future research opportunities for theoretical-practical advancement of the field. The field is structured on consolidated theoretical pillars, evolving towards an integrative multidisciplinary approach. AI offers significant transformative potential, but requires responsible implementation considering technological, pedagogical and ethical aspects. Systematic research is essential to maximize benefits while minimizing risks, promoting effective and equitable AI integration in higher education through robust scientific evidence. First systematization of the field's intellectual structure via co-citation and emerging frontiers via bibliographic coupling. Operational framework offers practical guide for institutional implementation. Research agenda directs future investigations. Contributes theoretically by identifying gaps and taxonomies, and practically by providing guidelines for educational managers on AI implementation policies, procedures and strategies based on scientific evidence.
A. A. de Lima, C. K. Yamaguchi, M. D. de Oliveira et al.· Revista edUCA - Revista Mult...· 1 citation
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
