Artificial Intelligence Adoption in Higher Education: A Bibliometric Analysis of Theoretical Frameworks, Emerging Trends, and Global Research Patterns

Raktimabh Kakati and Deep Jyoti Gurung
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Raktimabh Kakati: Assam Skill University, Applied Management, Mangaldai, Assam, India
Deep Jyoti Gurung: Assam Skill University, Applied Management, Mangaldai, Assam, India

Diginomics, 2026, vol. 5, 262

Abstract: Introduction: Faculty adoption represents a critical determinant of successful AI integration in higher education, yet the intellectual landscape of this research domain remained fragmented and inadequately mapped. This study examined the theoretical frameworks, thematic structures, and knowledge gaps characterizing AI adoption research in higher education contexts.Methods: A bibliometric analysis was conducted on 43 peer reviewed articles retrieved from Scopus (2010 2025). Data were analyzed using Biblioshiny, employing co occurrence analysis, bibliographic coupling, network analysis, and thematic mapping to reveal the field's intellectual structure.Results: Publication output demonstrated exponential growth, increasing from 5 articles in 2022 to 19 in 2025. The Technology Acceptance Model emerged as the dominant theoretical framework, with perceived usefulness and perceived ease of use serving as foundational constructs. Network analysis identified five thematic clusters: AI integration in educational contexts, TAM based adoption frameworks, ChatGPT and generative AI applications, decision support systems, and technology readiness. Geographic analysis revealed concentration in Asian contexts (China, Indonesia, Malaysia, India), representing over 50% of publications. Critical gaps were identified in ethical considerations, organizational dimensions, and cross cultural perspectives.Conclusions: The findings underscored the need for multi level interventions addressing individual perceptions, institutional infrastructure, and policy frameworks simultaneously. ChatGPT's emergence as a distinct research node highlighted challenges in developing theoretical frameworks responsive to rapidly evolving technologies. Future research should incorporate ethical awareness, longitudinal adoption patterns, and discipline specific integration challenges to realize AI's transformative potential in higher education.

Keywords: Artificial Intelligence; Higher Education; Technology Acceptance Model; Bibliometric Analysis; Faculty Adoption; Generative AI (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cwg:digino:v:5:y:2026:id:262

DOI: 10.62486/digi2026262

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