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August 5, 2025Asian Journal of Research in Computer ScienceOpen Access

Understanding AI Adoption in Higher Education: A Systematic Review of Technology Acceptance Model, Technology Readiness Index, and the Integrated Technology Readiness and Acceptance Model

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Authors

NMNiti MittalGBGeetanjali BatraJawaharlal Nehru UniversityRSRajeev SijariyaJawaharlal Nehru University

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Implication

Systematic review examines AI adoption factors in higher education, suggesting significant predictors like perceived usefulness and ease of use.

Key Points

  • Core predictors for AI adoption include perceived usefulness and ease of use, influencing behavioural intention.
  • The systematic review analyzed 25 studies, with a focus on technology acceptance and readiness models like TAM and TRI.
  • Research identified a gap between intention to use technology and actual technology usage in higher education settings.
  • Key research gaps include a shortage of longitudinal studies and underrepresentation of inclusive education professionals.

Cite This Study

Mittal et al. (2025) studied this question.

synapsesocial.com/papers/689a0f93e6551bb0af8d132bhttps://doi.org/10.9734/ajrcos/2025/v18i7729
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  3. 3AI Adoption and Educational Effectiveness in Emerging Higher Education Institutions: The Moderating Role of Digital Literacy and Institutional Support2025 · 8 citations
  4. 4Adoption of Artificial Intelligence and Sustainable Learning Outcomes in Engineering Education: Evidence from the Technology Acceptance Model2026
  5. 5Navigating the Adoption of Artificial Intelligence in Higher Education2024 · 16 citations