Generative AI in Higher Education: Opportunities, Challenges and Institutional Readiness: A Study of Faculty and Students
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Abstract
Purpose: Generative artificial intelligence (GenAI) tools like ChatGPT, Gemini, and Copilot are transforming the landscape of teaching, learning, assessment, and research within universities. However, there is considerable variation among institutions in their preparedness to manage and facilitate the integration of these technologies. This study investigates how perceived opportunities and challenges, along with institutional readiness, influence attitudes, behavioral intentions, and the utilization of GenAI by faculty and students at universities.
Design: Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT2) alongside the Technology–Organisation–Environment (TOE) framework, we developed and assessed a model comprising seven hypotheses. A cross-sectional survey was conducted with 420 participants (168 faculty members and 252 students) from 12 higher education institutions. The data were analyzed employing exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling using IBM SPSS AMOS software. Additional techniques included bias-corrected bootstrapping, moderation analysis, and multi-group comparison.
Findings: The measurement model exhibited a strong fit (χ²/df = 1.53, CFI = .968, RMSEA = .036). Both perceived opportunities (β = .50) and perceived challenges (β = −.23) were significant predictors of attitude; this attitude subsequently influenced behavioural intention (β = .46). Institutional readiness directly affected intention (β = .25) and moderated the relationship between intention and use (β = .14). Attitude served as a partial mediator for the impact of opportunities while fully mediating the effect of challenges. Notably, institutional readiness was found to be greater among faculty compared to students as well as in private institutions.
Originality and implications: This research merges individual-level factors influencing adoption with an institutional-readiness framework two components that are frequently examined independently. The results indicate that clarity in policy directives, development programs for faculty, and redesigning assessments are equally crucial as individual motivation in promoting responsible usage of GenAI technologies.
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