Aug 2026· International Journal of Education in Mathematics Science and Technology· 0 citations
TL;DR
A bibliometric analysis was conducted to examine publication trends, disciplinary distributions, international collaborations, and their alignment with the United Nations Sustainable Development Goals (SDGs) to indicate a rapid acceleration of research output post-2019.
Abstract
This study investigates the evolution and conceptual framework of research connecting artificial intelligence (AI) with scientific thinking in science education. Utilizing a dataset of 21,874 publications retrieved from the Web of Science, a bibliometric analysis was conducted to examine publication trends, disciplinary distributions, international collaborations, and their alignment with the United Nations (UN) Sustainable Development Goals (SDGs). The results indicate a rapid acceleration of research output post-2019, marking the consolidation of AI-related inquiry within educational and cognitive domains. Although the field remains anchored in computer science and engineering, there is a notable rise in interdisciplinarity through contributions from education, psychology, and philosophy. Global participation is concentrated in technologically advanced regions, while institutional patterns highlight collaborative networks among leading universities and research centers. SDG classifications reveal connections with health, education, innovation, and environmental sustainability. Overall, the study depicts a dynamically expanding domain where AI functions not only as a technological innovation but also as an epistemic framework that reshapes reasoning, inquiry, and reflective judgment in science education. The findings offer both a conceptual and empirical basis for linking technological capabilities with the epistemic and ethical dimensions of learning in an AI-driven world.
The rapid advancement of artificial intelligence (AI) has accelerated its integration with psychology, fostering profound transformations in research paradigms within the discipline. However, this interdisciplinary development also faces challenges related to technological ethics, data privacy, and insufficient cross-disciplinary collaboration. Using CiteSpace as the bibliometric visualization tool, this study systematically analyzed 347 publications indexed in the China National Knowledge Infrastructure (CNKI) database from 1997 to 2025 to reveal the dynamic knowledge structure and research evolution in this field. The results indicate that current research hotspots primarily focus on AI-enabled mental health services and intelligent education, cognitive and emotional modeling, optimization of human resource management, and governance of ethical risks. The technological trajectory demonstrates a clear transition from theoretical exploration to practical applications, while policy initiatives have significantly promoted the precision of mental health screening and intervention. Future research should prioritize the establishment of a multi-level ethical governance framework and strengthen international collaborative mechanisms to facilitate the high-quality development of mental health services and the integration of interdisciplinary theories. The findings provide valuable insights for optimizing resource allocation and promoting the sustainable development of AI-driven mental health services.
Meng Guo, Na Ni· Journal of Artificial Intell...· 0 citations
The rapid emergence of Generative Artificial Intelligence (GenAI) has transformed higher education by reshaping teaching, learning, assessment, and institutional practices. Despite the rapid growth of scholarly publications, existing bibliometric studies have largely focused on descriptive indicators, providing a limited understanding of the field's intellectual foundations, conceptual evolution, and theoretical development. This study addresses this gap by conducting a comprehensive bibliometric and science mapping analysis of 3,214 Scopus-indexed publications on Generative AI in higher education published between 2020 and 2026. Following the PRISMA 2020 framework, bibliographic data were analyzed using VOSviewer version 1.6.20 to examine publication trends, co-citation networks, and keyword co-occurrence patterns. The findings reveal exponential growth in research following the widespread adoption of large language models, with publication output expanding rapidly since 2023. Co-citation analysis identified five major intellectual domains encompassing AI-assisted learning applications, student engagement, methodological and theoretical foundations, AI-supported language learning, and technology acceptance. Co-word analysis further revealed four dominant conceptual themes: pedagogical integration, learner-centered research, computational technologies, and ethical governance. Collectively, these findings demonstrate that Generative AI research has evolved from technologyoriented investigations to a multidisciplinary educational ecosystem that emphasizes pedagogical innovation, institutional transformation, and responsible AI implementation. Building upon these empirical findings, the study proposes the AI Pedagogical Knowledge Ecosystem Framework, which integrates technological innovation, pedagogical transformation, learner engagement, institutional governance, and educational outcomes into a unified conceptual model. By combining co-citation and co-word analyses within a single science mapping framework, this study extends previous bibliometric research beyond descriptive mapping toward theory-informed conceptual development and knowledge synthesis. The findings provide valuable guidance for educators, higher education institutions, policymakers, instructional designers, and researchers seeking to support the responsible and sustainable integration of Generative AI in higher education.
M. A. P. Galang· International Journal of Edu...· 0 citations
Artificial intelligence (AI) is fundamentally reshaping accounting practice and redefining the landscape of accounting education. Although scholarly interest has grown substantially, the intellectual structure of this research area remains fragmented and insufficiently theorized. This study undertakes a comprehensive bibliometric and performance analysis of 259 Scopus-indexed publications (2010–2025) using PRISMA guidelines to map the field's evolution systematically. The findings reveal a dramatic surge in publications post-2020, with research heavily concentrated in Computer Science (127 publications) rather than mainstream accounting education journals. Furthermore, geographic contributions are highly centralized, with China (71 publications) and the United States (45 publications) dominating. Crucially, while the application of AI is expanding, its integration with established learning theories (such as the Technology Acceptance Model and constructivism) remains remarkably scarce, with the existing literature prioritizing technological adoption over empirical pedagogical effectiveness. These findings provide a structured, evidence-based foundation for educators, academic institutions, and professional bodies to design sustainable, pedagogically aligned AI curricula.
Unknown authors· International Journal of Adv...· 0 citations
Given the diversity of theoretical approaches to mathematics learning, this study aims to characterize the predominant theoretical perspectives on the understanding of mathematical concepts within the scientific literature, as well as to identify the collaboration networks and intellectual structure of research in this field. A bibliometric and social network analysis was conducted on 4,266 records from the Scopus database (1980–2024). The findings reveal a dual structure: while the discipline’s intellectual base remains anchored in educational psychology theories (Pekrun, Eccles), the current research front has evolved toward specialized didactic frameworks such as the Onto-Semiotic Approach and Embodied Cognition. The results indicate that the discipline is undergoing a phase of professionalization and theoretical specialization, although high fragmentation and a critical geographic gap persist, excluding emerging regions like Latin America from central collaboration nodes. It is concluded that an epistemological transition is occurring, moving from classical socio-constructivism toward neuro-cognitive and multimodal approaches. These findings underscore the need to integrate fragmented theoretical frameworks and promote transregional collaboration policies to diversify global theoretical production.
L. Navarro-Ibarra, Omar Cuevas-Salazar, Jeanneth Milagros Valenzuela-Ochoa et al.· International Electronic Jou...· 0 citations
Computational thinking (CT) has become an important competency in mathematics education (ME), supporting students’ problem-solving, reasoning, and algorithmic thinking. However, the development of research on CT in ME, including publication trends, influential studies, collaboration patterns, and research themes, has not been comprehensively mapped. This study aims to map the research landscape of CT in ME through a descriptive bibliometric analysis. Publication data were retrieved from Google Scholar, Crossref, and Scopus using the keywords computational thinking, education, and mathematics. After relevance screening, 140 publications from 2016 to 2026 were selected for analysis. Publication trends, citation performance, collaboration networks, and keyword co-occurrence were analyzed using VOSviewer. The findings show a substantial increase in publications since 2020, with the highest output recorded in 2023 and 2024. Citation analysis identifies highly cited studies that have contributed to the conceptual development of CT in ME, while the Journal of Pedagogical Research emerged as the most productive journal. The collaboration network indicates emerging but still limited international collaboration, particularly involving researchers from Sweden, Denmark, and the United Kingdom. Keyword co-occurrence analysis reveals three interconnected research areas: CT implementation in mathematics learning, core CT processes such as decomposition, pattern recognition, abstraction, and generalization, and CT assessment and measurement. The findings show that CT research remains focused on measurement and assessment, while pedagogical approaches, creativity, teacher education, and elementary mathematics education are less explored, highlighting the need for systematic, contextual approaches that connect CT processes with meaningful mathematics learning.
Kufita Rachman, N. Andrijati· Jurnal Riset dan Inovasi Pem...· 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.
