Jul 2026· International Research Journal of MMC· 0 citations· 33 references
TL;DR
A five-phase framework comprising Strategic Conceptualisation, Systematic Literature Synthesis, Methodology Selection, Governance, Ethics and Trust, and Continuous Reflection and Feedback is proposed, positioning the human touch, curiosity, critical thinking, critical thinking, creativity, contextual expertise, and ethical judgement, as the foundation of responsible AI-assisted research planning.
Abstract
Artificial intelligence (AI), particularly generative AI and large language models (LLMs), is rapidly transforming research planning by enhancing literature synthesis, research problem formulation, methodology selection, and workflow management. Despite this rapid adoption, limited evidence exists regarding the intellectual structure, thematic evolution, and responsible integration of AI within research planning. Therefore, this study systematically maps the scientific landscape of AI-assisted research planning through a bibliometric analysis of publications indexed in the Scopus database between 2017 and 2026. Bibliographic data were analysed using Biblioshiny (Bibliometrix) and VOSviewer to examine publication trends, influential contributors, collaboration networks, intellectual structure, and thematic development. The findings reveal a rapidly expanding research domain, with an annual publication growth rate of 51.08%, reflecting the increasing integration of AI into research planning. Science mapping identifies research management, artificial intelligence, machine learning, and large language models as the dominant research themes, while thematic evolution demonstrates a shift from foundational AI technologies towards AI-enabled research management and workflows. However, the analysis also reveals a significant conceptual imbalance, with technological advancements outpacing the development of governance, trust, ethics, and human–AI collaboration. To address this gap, the study proposes a five-phase framework comprising Strategic Conceptualisation, Systematic Literature Synthesis, Methodology Selection, Governance, Ethics and Trust, and Continuous Reflection and Feedback, positioning the human touch, curiosity, critical thinking, creativity, contextual expertise, and ethical judgement, as the foundation of responsible AI-assisted research planning. The study contributes both a comprehensive knowledge map and a human-centred conceptual framework that provide practical guidance for researchers, scholars, and policymakers seeking to promote the responsible, transparent, and sustainable integration of AI throughout the research planning process.
Artificial intelligence (AI) is increasingly integrated into human resource management (HRM) functions as organizations accelerate their digital transformation. However, comprehensive studies mapping the intellectual development, thematic evolution, and emerging directions of AI-HRM research remain limited. This study aims to examine the growth, research trends, and future directions of AI applications in HRM through bibliometric analysis. A quantitative bibliometric approach was employed using literature retrieved from the Scopus database based on predefined search terms and screening criteria. Bibliometric performance and thematic analyses were conducted using Biblioshiny within the bibliometrix package, while bibliometric networks were visualized using VOSviewer. The analysis identified 856 publications indexed between 1991 and 2027 across 358 publication sources, involving 2,452 authors and an international collaboration rate of 33.18%. The field has experienced substantial growth, particularly since 2019. Keyword analysis identified artificial intelligence, human resource management, and machine learning as the dominant research themes, while thematic evolution revealed a shift toward increasingly specialized topics, including generative AI, large language models, and digital transformation. These findings demonstrate that AI-HRM research is evolving in parallel with technological advances and the expanding integration of AI into HRM practices. This study contributes a comprehensive mapping of the field’s intellectual and thematic development and provides a foundation for researchers to identify emerging topics, underexplored areas, and future research opportunities in AI-enabled HRM.
R. Pratama, Edris Santoso, P. Sumantri et al.· Journal multidisciplinary sc...· 0 citations
This study aims to map and interpret the intellectual landscape of research on artificial intelligence (AI) in talent management. It identifies the dominant themes and research trajectories shaping this domain.
Using a latent Dirichlet allocation topic modeling approach, a machine learning-based text-mining technique, a corpus of 243 journal articles published between 2010 and 2025 were analysed.
Eight major research themes emerged: AI Adoption and Cultural Change, Digital Transformation and Smart Technologies, Analytical and Bibliometric Approaches, AI-based Learning and Capability Development, Sustainable HR, AI-enabled Recruitment and Talent Acquisition, AI-driven talent management (TM) practices and Employee Experience and Behavioural Change. Research has evolved from descriptive and technical explorations to human-centric and ethical considerations of AI in HRM.
Methodologically, this paper demonstrates the value of machine learning-based text analytics as a robust and scalable approach for synthesising literature. Theoretically, this study maps the thematic architecture of AI-enabled TM research through a socio-technical perspective and develops three conceptual propositions for future testing. From a managerial perspective, the findings highlight that the real value of AI in TM extends beyond operational automation. It encompasses strategic talent enablement, continuous learning and sustainable performance management.
This study provides data-driven syntheses of AI and talent management research. It integrates bibliometric mapping with topic modeling. Furthermore, it advances a socio-technical perspective of AI-enabled HRM, conceptualising AI as both a technological and strategic capability.
Priya Chaudhary· The International Journal of...· 0 citations
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.
Konstantinos Karampelas· International Journal of Edu...· 0 citations
This review highlights the field's strong interdisciplinary character and reveals the current challenges that the sector faces, and outlines a systematic research program to guide further studies of the implementation, impact, and problems of GenAI in enterprises and communities.
Majdouline Attaoui, Wissal Attaoui, Anas Moukrim et al.· International journal of mul...· 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
Despite the rapid integration of artificial intelligence (AI) feedback into ESL/EFL writing pedagogy, research providing a comprehensive overview of how the field has evolved, diversified, and matured remains limited. This study examines the development of AI feedback research in ESL/EFL writing from 2000 to 2025 through a chronological bibliometric analysis. Records were retrieved from Scopus and the Web of Science Core Collection and screened through a PRISMA-style process involving identification, deduplication, title and abstract screening, and full-text eligibility assessment. After screening, 282 empirical studies were included in the final dataset. Descriptive statistics and VOSviewer-based visualizations were used to analyze publication growth, major journals, countries/regions, institutions, highly cited studies, productive authors, keyword patterns, AI tools, feedback-focus categories, and methodological features. The results show that the field remained limited for many years but expanded sharply in 2024 and 2025. Research output was concentrated in a small group of journals and showed a strong presence of Asian EFL contexts, especially China and Hong Kong. The findings also indicate that the field is shaped by both earlier automated writing evaluation traditions and newer generative AI research. ChatGPT was the most frequently reported tool, while grammar, organization, vocabulary, and content were the most common feedback focuses. Methodologically, mixed-method research and quasi-experimental designs were especially visible. The study provides a structured overview of the field and points to the need for more transparent reporting of AI tools, feedback types, revision procedures, and higher-level writing features such as cohesion, coherence, argumentation, and development.
Wenpeng Li, M. Saeed, Rozita Che Omar· International Journal of Tec...· 0 citations
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