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.
Abstract
Abstract: Artificial Intelligence (AI) emerged as a transformative technology in Higher Education, reshaping active and hybrid learning methodologies. This study conducts a bibliometric analysis of recent literature on the integration of AI into active b-learning methodologies in the university context. A six-stage bibliometric methodology was employed, using Scopus-indexed publications from 2015 to 2024. PRISMA criteria were applied for study selection, and VOSviewer, Bibliometrix, and Microsoft Excel were used to process and visualize the data. Results indicate exponential growth in scientific output related to AI and active methodologies, particularly from 2020 onwards. A total of 167 relevant documents were identified, with conference papers being the most frequent publication type. The dominant themes included personalized learning, intelligent gamification, automated flipped classrooms, and AI-supported project-based learning. The analysis also identified the most influential authors, journals, and documents, as well as key academic collaboration networks. Findings suggest that the convergence between AI and active learning represents a promising path for pedagogical innovation in Higher Education. However, several challenges remain, including ethical considerations, technical limitations, and teacher training. This study offers a comprehensive overview of the current state of research, highlighting the need for further investigation into pedagogical, ethical, and contextual impacts of digital transformation in education.
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.
Noor Fadzilah, Ab Rahman, Nurkaliza Khalid· e-Jurnal Penyelidikan dan In...· 0 citations
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.
Objectives. As artificial intelligence increasingly enters educational practice, PE pedagogy faces a distinct question that extends beyond online, blended, or technology-enhanced delivery. This study mapped research on artificial intelligence in PE pedagogy from 2015 to 2025, focusing on publication growth, conceptual structure, interdisciplinary foundations, pedagogical applications, and emerging concerns.
Materials and Methods. Using a bibliometric and thematic review design, bibliographic data were retrieved from Scopus and analyzed through Bibliometrix/Biblioshiny and VOSviewer. After filtering and data cleaning, the final corpus comprised 1,000 documents published across 389 sources.
Results. The results revealed a marked post-2020 expansion, indicating growing scholarly attention to AI-related pedagogical applications in PE. The field was strongly interdisciplinary, spanning PE, educational technology, computer science, engineering, and health-related domains. Conceptual and thematic analyses showed that the literature is anchored in PE, artificial intelligence, machine learning, deep learning, data mining, physical education teaching, virtual reality, and movement-related analytics. Bibliographic coupling and co-citation analyses further indicated that the field is developing through the convergence of computational innovation and pedagogical application rather than within a single disciplinary tradition.
Conclusions. The study positions AI in PE pedagogy as an emerging computational-pedagogical research domain. The evidence should be interpreted as structural and thematic mapping, not as proof of instructional effectiveness. As a conceptual contribution, the review advances a Pedagogically Accountable AI in PE Framework centered on computational function, pedagogical translation, embodied movement learning, human agency, and ethical accountability.
Keywords: artificial intelligence, bibliometric analysis, emerging concerns, pedagogical applications, physical education.
J. Lobo· Physical Education Theory an...· 0 citations
The findings suggest that future research and practice should focus on how generative AI can be used effectively, responsibly, and sustainably in authentic higher education settings, with attention to learning quality, long-term effects, fairness, data ethics, and governance.
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
Objective: This research aims to reveal the development of scientific output in the field of artificial intelligence in higher education, its thematic trends, influential publications, and international collaboration networks. The study aims to contribute to future research and policy-making processes by mapping the intellectual structure of the field.Method: The research was conducted using a bibliometric analysis design. Data were obtained from 196 articles scanned in the Scopus database and meeting the specified criteria. VOSviewer (v1.6.20) software was used for data analysis. Publication trends, co-authorship, citations, keyword association, and international collaboration networks were analyzed. To increase validity and reliability, pre-specified inclusion and exclusion criteria were applied, the analysis process was reported in detail, and cross-validation was performed among researchers.Results: The findings show that artificial intelligence research in higher education has increased rapidly, especially since 2022, and that publications constitute approximately two-thirds of the total studies in 2024. Forty-five percent of the studies fall within the social sciences, followed by computer science and engineering. Thematic analysis identified ethics, ChatGPT, generative AI, learning, teaching, and educational technologies as the most frequently co-occurring concepts.. Collaboration analyses revealed that the United States, Australia, and the United Kingdom hold leading positions in international research networks.Conclusion and Recommendations: The research shows that AI studies in higher education have an interdisciplinary structure and that technological developments are addressed together with pedagogical, ethical, and managerial dimensions. The findings reveal that AI is not only a technological innovation but also that publications from 2024 alone account for approximately two-thirds of the dataset. Future research should incorporate comparative bibliometric studies across multiple databases and expand qualitative and mixed-methods research into the long-term pedagogical and cognitive effects of AI.
