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.
Abstract
This study aimed to map research trends, knowledge structures, and emerging frontiers of generative artificial intelligence in higher education. Based on 774 publications retrieved from the Web of Science database from 2021 to 2026 (Retrieval Date: May 16, 2026), VOSviewer and CiteSpace were used to analyze publication trends, core authors, journals, institutions, keyword co-occurrence, thematic clusters, co-citation networks, keyword time zones, and burst terms. The results show rapid growth after 2023, with a publication peak in 2025. Keyword co-occurrence and clustering analyses identify four interconnected themes: educational processes and learner behavior, technology acceptance and use, AI technology applications and methods, and educational outcomes and assessment. Co-citation and temporal analyses indicate that the field is supported by research on ChatGPT and large language models, technology acceptance theories, and application-oriented studies in contexts such as language learning, academic writing, medical education, assessment, and feedback. The research frontier is shifting from tool adoption and student perceptions toward cognitive load, self-regulated learning, learning analytics, feedback mechanisms, assessment redesign, academic integrity, and institutional policy. These 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.
This study examines the development and educational implications of artificial intelligence (AI) in higher education through a combination of bibliometric analysis and experimental evidence. First, publications indexed in the Scopus database from 2000 to 2024 were analyzed to map the evolution of research on AI in higher education, with attention to publication trends, thematic concentrations, and influential studies. Text mining was conducted on titles and abstracts, and term frequency-inverse document frequency (TF-IDF) weighting was used to identify representative terms. K-means clustering and Latent Dirichlet Allocation (LDA) topic modeling were then applied to detect major research themes, while PageRank analysis of the citation network was used to identify publications with high structural influence in the field. Alongside the bibliometric analysis, the study conducted an experimental investigation of ChatGPT as a formative assessment tool. Student responses were submitted to ChatGPT to generate automated feedback and grades, and the outputs were examined in terms of feedback type, instructional value, grading consistency, and agreement with human evaluation. The results suggest that students receiving ChatGPT-supported assistance showed better learning performance than those without AI support. The feedback generated by ChatGPT contained corrective, explanatory, and motivational elements, indicating its potential to provide both cognitive and affective support during learning. In addition, the comparison between AI-generated grades and human-assigned grades showed a high level of alignment, with limited evidence of systematic bias.
Yinfeng Zhang, Melissa Ng Lee Yen Abdullah· International journal of com...· 0 citations
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.
Sergio Sargo Lopes, M. Lousã, Jorge Azevedo Simões et al.· Revista EDaPECI· 0 citations
This study aims to analyze the scientific literature on artificial intelligence (AI) applications in foreign language education using a bibliometric approach. A total of 1,188 articles published between 2020 and 2025 in the Web of Science Core Collection were examined based on the keywords “AI-assisted language learning,” “NLP in education,” and “intelligent tutoring systems.” The analysis was conducted using VOSviewer software, which enabled the examination of country-author-institution distributions, citation networks, keyword clusters, and co-authorship structures. The results reveal that China, the United States, and the UAE lead the field in publication volume. Influential scholars such as Barrot (2023), Davis (2022), and Bandura (2021) were identified with high citation impacts. Frequently recurring keywords include “ChatGPT,” “critical thinking,” “learning motivation,” and “writing skills.” Co-authorship mapping reveals limited interdisciplinary collaboration and predominantly region-based partnerships, while the absence of sustained international co-authorship highlights the fragmented and locally bound nature of research collaboration in this field. The findings highlight the evolving role of AI in language education, particularly in adaptive learning, student perception, and instructional innovation. This study contributes to the field by quantitatively identifying epistemological patterns and research gaps, offering a strategic roadmap for future studies in AI-supported language learning.
Kılıç Köçeri, A. Akçay· Eğitim Teknolojisi Kuram ve...· 0 citations
A temporal shift toward generative AI and contextual inquiry as the most recent research concerns is confirmed, and the complete absence of publications from Africa, Latin America, and South Asia signals a critical equity gap in the global research agenda.
Helga Merilla Zafirah Widad, A. Prasetya, Bambang Subali et al.· Journal of Innovation and Re...· 0 citations
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.
Noor Fadzilah, Ab Rahman, Nurkaliza Khalid· e-Jurnal Penyelidikan dan In...· 0 citations
The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.
L. Judijanto· West Science Interdisciplina...· 0 citations