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@article{193067,
        author = {Nikhil Mathew Plackattu and Madhukumar. PS and Tiya Elizabeth Jose and Takhellambam Himat Singh},
        title = {Adoption And Usage Patterns of Generative Artificial Intelligence in Higher Education: A Bibliometric Analysis of Scopus-Indexed Literature},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {4021-4037},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193067},
        abstract = {The rapid diffusion of Generative Artificial Intelligence (GenAI), particularly large language models such as ChatGPT, has significantly reshaped higher education. Students and academic staff increasingly use AI-based systems for learning, assessment, research support, and pedagogical design. Despite growing empirical studies on AI adoption and academic integrity, a consolidated bibliometric synthesis of this emerging domain remains limited. This study addresses that gap through a systematic bibliometric analysis of 33 Scopus-indexed journal articles published between 2023 and 2025. Using the Bibliometrix package in RStudio, the analysis maps publication trends, influential contributors, thematic clusters, and conceptual structures through co-occurrence networks and Multiple Correspondence Analysis (MCA).
Findings indicate exploratory emergence in 2023 (4 publications), moderate growth in 2024 (5 publications), and strong consolidation in 2025 (24 publications), with 72.7% indexed under 2025. “Artificial intelligence” functions as the structural hub of the knowledge network, demonstrating the highest betweenness centrality (82.800) and PageRank (0.189). Thematic mapping identifies student-centered motor themes, AI integration as a foundational theme, governance and faculty-related niche themes, and emerging GenAI-focused inquiries. Temporal evolution reflects a shift from exploratory discourse toward structured pedagogical and governance frameworks. The study contributes to theory by aligning findings with the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Innovation Diffusion Theory, offering strategic insights for institutions navigating AI integration.},
        keywords = {Generative AI, ChatGPT, Higher Education, AI Adoption, Bibliometric Analysis, Students, Academic Staff, Scopus},
        month = {February},
        }