Jul 2026· Journal of Innovation and Research in Primary Education· Vol 5, pp. 5154-5167· 0 citations
TL;DR
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.
Abstract
Research on artificial intelligence (AI) in elementary education has grown substantially, yet a systematic bibliometric mapping of this field within the post-generative AI era remains absent. This study employed a bibliometric analysis of 158 Scopus-indexed articles published between 2022 and 2026, retrieved using a structured TITLE-ABS-KEY query. Descriptive analysis was performed via the Scopus Analyze feature, while keyword co-occurrence mapping was conducted using VOSviewer software (version 1.6.20.0) with a minimum term occurrence threshold of 10. Publications increased sharply from 10 in 2022 to 73 in 2025. China was the most productive country (28.5%), and The Education University of Hong Kong led institutionally. Social Sciences (38.8%) and Computer Science (28.0%) dominated subject areas. Keyword co-occurrence analysis identified three clusters: AI effects on student learning and engagement, AI integration frameworks and literacy, and generative AI and student motivation. Overlay visualization confirmed a temporal shift toward generative AI and contextual inquiry as the most recent research concerns. The findings reflect a field catalyzed by the emergence of generative AI tools, with research geographically concentrated in East Asian contexts and structurally evolving from outcome measurement toward motivational and pedagogical inquiry. The complete absence of publications from Africa, Latin America, and South Asia signals a critical equity gap in the global research agenda.
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
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
Artificial Intelligence (AI) as a representation of technological advancement, has been recognized for its potential to positively and negatively impact the educational sphere. In mathematics education, AI revolutionizes learning by offering students more flexible and adaptive learning experiences. This study aims to provide insights into the impact of AI integration in mathematics education through a systematic literature review (SLR). The primary focus of this research includes trends in AI usage based on year, country, and education level, types of AI integration, and the impacts and challenges encountered in implementing AI in mathematics learning. The research data comprises empirical articles collected using the PRISMA approach, drawing from databases such as Scopus, ERIC, and SAGE over the past five years. This results in the analysis of 13 selected articles. The findings reveal dynamic trends in AI utilization, particularly in Indonesia and Australia. In Indonesia, AI is primarily used to expand access to online education, while in Australia, it is leveraged for personalized learning. AI is predominantly integrated at the middle school and university levels, with ChatGPT as the primary tool to assist students in understanding mathematical concepts. The benefits of AI include improvements in cognitive, affective, and teaching abilities. The challenges of AI identified include low user awareness, limited educator skills, cognitive impacts on students, and technological access disparities that exacerbate educational inequalities.
Lukman Hakim Muhaimin, T. Turmudi, Asllan Vrapi et al.· Knowledge Management & E...· 0 citations
The integration of Artificial Intelligence (AI) into education is rapidly transforming teaching methods and educators’ professional roles. However, while the literature on AI in education has expanded significantly, prior bibliometric studies have primarily focused on broad technological perspectives, higher-education contexts, or student-centred learning. Moreover, limited attention has been devoted to how AI specifically shapes Teacher Professional Development (TPD) and its contribution to enhanced teaching practices. Thus, this study addresses this gap by conducting a comprehensive bibliometric analysis of research at the intersection of AI-focused TPD and classroom pedagogy. Accordingly, a total of 110 peer-reviewed publications (2018-2025) were analysed using descriptive bibliometrics, citation performance indicators, and science-mapping techniques, including co-authorship and keyword co-occurrence networks. Additionally, findings reveal a sharp acceleration in scholarly interest from 2022 onwards, indicating a rapidly maturing field driven by global educational digitalisation. The United States (US), Hong Kong, and Saudi Arabia emerge as leading contributors, while journals such as Education Sciences and Computers and Education: Artificial Intelligence serve as influential publication outlets. Specifically, thematic mapping identifies three dominant research clusters: (1) AI-enhanced teacher learning and competencies, (2) Generative AI (GenAI) applications in professional practice, and (3) pedagogical transformation through data-driven and intelligent systems. Overall, this study highlights a growing shift from technical adoption toward research exploring teachers’ readiness, ethical awareness, and pedagogical innovation when integrating AI. Likewise, the findings provide a valuable evidence base to guide future TPD initiatives aimed at strengthening AI-aligned teacher capabilities and improving instructional quality.
Unknown authors· International journal of tec...· 0 citations
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
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