Understanding AI Adoption in Higher Education: A Systematic Review of Technology Acceptance Model, Technology Readiness Index, and the Integrated Technology Readiness and Acceptance Model - Mittal, Niti; Batra, Geetanjali; Sijariya, Rajeev (Asian Journal of Research in Computer Science, 2025)
Artificial Intelligence (AI) is reshaping education through numerous pedagogical possibilities. To reap its benefits, it is critical to understand the factors affecting its adoption by users. Among various Information Systems (IS) theories applied in this context, the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI) are the most prominent. This systematic literature review (SLR) examines 25 studies, 20 based on TAM, 3 on TRI, and 2 combining both (TRAM or TRITAM) to explore how Artificial Intelligence (AI) technologies are being adopted in higher education. PRISMA guidelines were followed to ensure transparency and reproducibility of search results. Data extraction focused on key parameters like study design, analysis technique, technological focus, population studied, sample size used, theoretical lens applied, factors explored, and the key findings. The findings reveal that cognitive factors, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU) are the most significant predictors of Behavioural Intention (BI) to adopt AI. Additionally, AI-specific constructs such as AI Literacy, AI Explainability, and Co-Creation Intention were also studied in a few studies. Actual technology use has been rarely measured, indicating a recurring intention–actual use gap. The review also highlights crucial research gaps, such as a lack of longitudinal studies, use of only self-reported measures, underrepresentation of inclusive education professionals and decision-makers, and absence of cross-cultural comparisons. Thus, by synthesizing core findings, this review offers practical guidance for designing inclusive, evidence-based AI adoption strategies in higher education.