Jul 2026· International journal of multidisciplinary research and analysis· 0 citations
TL;DR
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.
Abstract
In this systematic literature review, we aimed to identify and thoroughly analyze the existing scientific knowledge related to a notably emerging field of generative AI. In strict adherence to the SPAR-4-SLR protocol, we focus on 1,104 peer-reviewed articles from the Scopus database, published between 2015 and 2025. Using bibliometric and thematic mapping methods, we address two main research questions: the first concerns the major publication trends in GenAI research, and the second deals with the intellectual structures and thematic domains shaping the field. Our results indicate an abrupt increase in scientific production since 2022, a consequence of the launch of models such as GPT, DALL·E, and Stable Diffusion, which are becoming increasingly powerful. We further identify five main research clusters: the technological foundations of generative AI, where researchers focus on building and utilizing LLMs and deep learning; professional and educational applications; ethical and governance issues; AI-assisted creativity; and user perceptions. Additionally, we find that higher education plays a significant role in the area, both in the application of ideas and the exploration of relevant questions. This review highlights the field's strong interdisciplinary character and, at the same time, reveals the current challenges that the sector faces. We outline a systematic research program to guide further studies of the implementation, impact, and problems of GenAI in enterprises and communities.
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.
Niranjan Devkota, M. Siddique, Dipendra Karki et al.· International Research Journ...· 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
The rapid proliferation of Generative AI (GenAI) tools—including large language models (LLMs) such as GPT-4, Claude, and Gemini—is fundamentally transforming the landscape of scholarly research, from hypothesis generation and literature synthesis to manuscript preparation, peer review, and research impact assessment. While these tools promise to democratize research productivity and accelerate scientific discovery, they simultaneously introduce profound challenges to the integrity, equity, and epistemological foundations of academic knowledge production. This paper presents a comprehensive mixed-methods investigation into the impact of GenAI on research evaluation and scholarly communication, combining a large-scale survey of 2,850 researchers, editors, and graduate students across 45 countries with a quantitative analysis of 18,000 manuscripts submitted to 12 peer-reviewed journals between 2022 and 2025. We find that GenAI adoption in research workflows has increased from 28% to 72% for literature review and from 18% to 54% for manuscript drafting over two years. AI-assisted manuscripts demonstrate 29 points higher clarity scores and 33 points higher structural coherence compared to unassisted manuscripts, but exhibit 8% lower citation accuracy due to hallucinated references. We further propose ScholarAI, a responsible GenAI framework for scholarly communication that integrates provenance tracking, citation verification, bias detection, and transparent authorship attribution. A controlled evaluation with 480 researchers demonstrates that ScholarAI improves manuscript quality by 46.8% while reducing ethical violations by 78.5% compared to unregulated GenAI use. We conclude with policy recommendations for institutions, publishers, and funding agencies navigating the GenAI transformation of scholarship
Ravi Ranjan, Ritesh Kandari, Anirudh Gupta, Divya Nigam· International Journal of Adv...· 0 citations
The rapid evolution of Generative AI (GenAI) has transformed the ways in which knowledge is created, shared, interpreted, and applied in organization and educational contexts. While earlier studies have often focused on the technical capabilities of GenAI or on the detection of synthetic content, less attention has been given to how these tools influence knowledge management processes at the individual and group levels. This study addresses that gap by examining how GenAI affects the production, transfer, validation, and use of knowledge among individuals and within collaborative settings. The aim of this study is to explore the current state of 'AI vs AI' by generating a dataset comprising texts, images, audio and video, utilizing a set of freely available AI tools. This pilot illustrative study was based on a two-stage strategy: in the first stage synthetic media was generated with different GenAI tools to mimic human creativity. A second stage, which involved a rigorous evaluation of AI-detection tools for each modality separately. Texts were created in three different GenAI tools and Images were prompted to mimic Renaissance paintings by Michelangelo and Raphael. Audio was generated with the purpose of mimicking interviews with the Renaissance painters. Videos, finally, were created to be deceptive deepfakes, placing persons in environments and situations where they never have been in real life. Findings provide a comparative analysis of tools' accuracy and false-positive rates that could act as valuable guidelines in the increasing flood of AI-generated content. This research contributes to the growing discussion on how organizations and institutions can manage the opportunities and risks associated with GenAI in knowledge-intensive environment. The study highlights the need for critical AI literacy, transparent knowledge practices, and governance mechanisms that ensure the responsible integration of GenAI into individual and collaborative knowledge work.
Anastasiia Iufereva, Peter Mozelius· European Conference on Knowl...· 0 citations
This systematic review investigates the application of artificial intelligence (AI) and machine learning (ML) in journalism and media practice from 2020 to 2026. Following PRISMA guidelines, we analysed 121 peer-reviewed articles from Scopus and Web of Science using a multi-method approach that combined qualitative thematic analysis, structural topic modelling (STM), and bibliometric network analysis. Four primary research domains emerged: news production and automation (38.0%), audience perception and content analysis (24.8%), ethical and legal considerations (19.8%), and meta-research and implementation studies (17.4%). Publication output accelerated sharply from 2023 onward, driven by the emergence of large language models and generative AI. The STM analysis confirmed the four-domain structure and revealed that legal-regulatory vocabulary pervades the literature across all categories, indicating a field-wide preoccupation with the institutional implications of AI. Key findings demonstrate that successful AI adoption depends on workflow redesign and human–machine collaboration rather than full automation; that audience evaluations of AI-generated content vary significantly across cultural contexts, with the machine heuristic—originating from Sundar’s MAIN model—playing a central mediating role; and that copyright frameworks for AI-generated news remain contested across jurisdictions. This study develops an integrated theoretical framework that maps directional relationships among the four research domains, identifies cultural context and disclosure practices as key moderators, and generates testable propositions for future investigation.
Gui Jun, Nasrullah Dharejo, M. Alivi· Journalism and Media· 0 citations
This PRISMA-guided bibliometric and abstract-level thematic review maps peer-reviewed industrial GAI research published from 2022 to 4 June 2026 contributes a reproducible cross-domain map, an overlap-aware synthesis, and stakeholder-specific guidance for trustworthy industrial GAI.
