Autonomous driving paper index
Driving quality education with generative AI: An integrated TTF-UTAUT in Saudi Higher education
One-line summary
The rapid advancement of Generative Artificial Intelligence (GenAI) offers transformative potential for higher education, yet its effective integration remains underexplored, particularly in developing contexts such as Saudi Arabia.
Engineering notes
The results show that task–technology fit, Performance Expectancy, effort expectancy, social influence and facilitating conditions significantly shape GenAI adoption, which in turn positively influences perceived educational quality outcomes.
Chinese explanation / 中文解读
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Original abstract
The rapid advancement of Generative Artificial Intelligence (GenAI) offers transformative potential for higher education, yet its effective integration remains underexplored, particularly in developing contexts such as Saudi Arabia. This study examines the factors influencing GenAI adoption among educators in Saudi universities and its contribution to Sustainable Development Goal 4 (Quality Education) by integrating the Task–Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT) models into an integrated framework comprising eight hypotheses (H1–H8). Using a quantitative design, data were collected from 413 academic staff across three universities through a structured questionnaire, and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that task–technology fit, Performance Expectancy, effort expectancy, social influence and facilitating conditions significantly shape GenAI adoption, which in turn positively influences perceived educational quality outcomes. The findings further demonstrate a dual mediation structure in which task–technology fit enhances performance expectations, and technology characteristics strengthen effort expectations, thereby reinforcing adoption intentions through an efficiency-oriented pathway grounded in task alignment and ease of use. These insights provide theoretical advancement by clarifying how TTF–UTAUT mechanisms operate in a GenAI-enabled higher education environment, and offer practical guidance for policymakers and institutional leaders seeking to design task-aligned, user-friendly, and ethically grounded GenAI strategies in support of Vision 2030 and SDG 4. Future research could extend this work through cross-country comparisons, longitudinal designs, and mixed-methods approaches that capture evolving GenAI practices in diverse higher education systems.
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