Generative AI Adoption in Higher Education: Examining Faculty Readiness, Trust and Institutional Support
Main Article Content
Abstract
Generative artificial intelligence (GenAI) is reshaping higher education, yet sustainable adoption
depends on more than access to tools. This study develops an integrated framework explaining
how faculty readiness, trust in AI systems, institutional support, perceived usefulness, and
perceived risk jointly influence faculty intention to adopt GenAI in teaching and academic work.
Drawing on technology acceptance, organizational support, trust calibration, and AI literacy
research, the paper specifies a cross-sectional survey design and a structural model suitable for
empirical validation. To demonstrate the analysis pipeline without misrepresenting uncollected
evidence, a fully disclosed synthetic dataset of 300 hypothetical faculty responses is used for
illustrative tables and charts. Illustrative estimates suggest positive relationships for readiness,
trust, and institutional support and a negative association for perceived risk. These numbers are
pedagogical examples, not observed effects. The study contributes a reproducible instrument
blueprint, construct operationalizations, mediation and moderation hypotheses, an ethics
protocol, and actionable institutional recommendations. Empirical data collection and
preregistered testing are necessary before any causal or generalizable claims can be made.
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References
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